Digital Image Research Articles

A Preliminary Evaluation of the Diagnostic Performance of a Smartphone-Based Machine Learning-Assisted System for Evaluation of Clinical Activity Score in Digital Images of Thyroid-Associated Orbitopathy.

Background: We previously developed a machine learning (ML)-assisted system for predicting the clinical activity score (CAS) in thyroid-associated orbitopathy (TAO) using digital facial images taken by a digital single-lens reflex camera in a studio setting. In this study, we aimed to apply this system to smartphones and detect active TAO (CAS ≥3) using facial images captured by smartphone cameras. We evaluated the performance of our system on various smartphone models and compared it with the performance of ophthalmologists with varying clinical experience. Methods: We applied the preexisting ML architecture to classify photos taken with smartphones (Galaxy S21 Ultra, iPhone 12 pro, iPhone 11, iPhone SE 2020, Galaxy M20, and Galaxy A21S). The performance was evaluated with smartphone-captured images from 100 patients with TAO. Three ophthalmology residents, three general ophthalmologists with <5 years of clinical experience, and three oculoplastic specialists independently interpreted the same set of images taken under a studio environment and compared their results with those generated by the smartphone-based ML-assisted system. Reference CAS was determined by a consensus of three oculoplastic specialists. Results: Active TAO (CAS ≥3) was identified in 28 patients. Smartphone model used in capturing facial images influenced active TAO detection performance (F1 score 0.59-0.72). The smartphone-based system showed 74.5% sensitivity, 84.8% specificity, and F1 score 0.70 on top three smartphones. On images from all six smartphones, average sensitivity, specificity, and F1 score were 71.4%, 81.6%, and 0.66, respectively. Ophthalmology residents' values were 69.1%, 55.1%, and 0.46. General ophthalmologists' values were 61.9%, 79.6%, and 0.55. Oculoplastic specialists' values were 73.8%, 90.7%, and 0.75. This smartphone-based ML-assisted system predicted CAS within 1 point of reference CAS in 90.7% using facial images from smartphones. Conclusions: Our smartphone-based ML-assisted system shows reasonable accuracy in detecting active TAO, comparable with oculoplastic specialists and outperforming residents and general ophthalmologists. It may enable reliable self-monitoring for disease activity, but confirmatory research is needed for clinical application.

Abstract PO4-07-05: Clinical evaluation of image-based risk profiling in breast cancer histopathology and comparison to an established gene expression assay

Abstract Background: Among oestrogen receptor (ER)-positive and human epidermal growth factor receptor 2 (HER2)-negative early breast cancer, a significant proportion of patients are categorised as intermediate risk, based on classic clinico-pathological variables, thus providing limited information to guide adjuvant chemotherapy decisions. Prognostic risk profiling is an integrated part of modern breast cancer diagnostics to provide additional risk information for this patient group. Among the established prognostic assays based on gene expression, the Prosigna assay is widely used and provides an individual risk of recurrence (ROR) score and identifies intrinsic subtypes with associated survival outcomes. Pioneering artificial intelligence-based precision diagnostics, Stratipath Breast, is a deep learning-based image analysis tool that utilises digitised histopathological whole slide images to stratify intermediate risk patients in terms of risk of recurrence. Materials and methods: The study included 234 invasive breast tumours from patients with early ER-positive HER2-negative breast cancer, clinically assessed as intermediate risk tumours and eligible for chemotherapy. All tumours had therefore previously been analysed by the Prosigna assay in clinical routine at point of diagnosis between 2020 and 2022 at the Karolinska University Hospital and Södersjukhuset, Stockholm, Sweden. Clinicopathological data including Prosigna results (ROR score, risk group and intrinsic subtype) were extracted from medical records, along with the corresponding archived haematoxylin and eosin (HE)-stained formalin-fixed paraffin-embedded tissue slides. The HE slides were subsequently digitised and analysed by the Stratipath Breast tool. The agreement between the two tests for risk stratification was evaluated in this real-world breast cancer case series. Results: The Prosigna assay classified 76 (32.5%), 110 (47.0%) and 48 (20.5%) tumours as low, intermediate and high risk, respectively. The Stratipath Breast analysis identified 116 (49.6%) tumours as low risk and 118 (50.4%) as high risk. Among Prosigna intermediate risk tumours, 52 (47.3%) were stratified as low risk and 58 (52.7%) as high risk by Stratipath Breast. The overall agreement between the two tests for low risk and high risk groups was 71.0%, with a Cohen’s linear kappa of 0.42. Twelve of the 48 Prosigna high risk cases were classified as Stratipath low risk. ROR scores were higher in the Stratipath high risk group compared to the low risk group (p &amp;lt; 0.001), across all cases as well as in the Prosigna intermediate group. Among the 176 histological grade (NHG) 2 tumours, 97 (55.1%) and 79 (44.9%) were stratified as Stratipath low risk and high risk, respectively, whereas 66 (37.5%), 83 (47.2%) and 27 (15.3%) were stratified as Prosigna low, intermediate and high risk, respectively. The majority of NHG1 (10 of 12) and NHG3 (37 of 46) tumours were stratified as Stratipath low risk and high risk, respectively. For both risk profiling tests, NHG and Ki67 proliferation index differed between risk groups. Conclusions: In this study of clinically assessed intermediate risk ER-positive HER2-negative breast cancer, we observed a moderate agreement between Prosigna and Stratipath Breast for low risk and high risk groups. In addition, image-based risk profiling stratified more of the NHG2 tumours as high risk. Citation Format: Stephanie Robertson, Yinxi Wang, Wenwen Sun, Emelie Karlsson, Sandy Kang Lövgren, Mattias Rantalainen, Johan Hartman. Clinical evaluation of image-based risk profiling in breast cancer histopathology and comparison to an established gene expression assay [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-07-05.

Abstract PO4-20-01: A PROSPECTIVE PILOT STUDY OF MAMMOGRAPHIC PREDICTION OF WOMEN’S RISK (M-POWER) IN NIGERIA

Abstract Background There have been no sustained efforts at stemming the rising incidence and mortality from breast cancer in sub-Saharan African countries like Nigeria. Mammography is the gold standard for breast cancer screening and is associated with improved survival particularly among women 50 to 69. Reports from various parts of Nigeria show very low mammography screening uptake. Causes include lack of geographical access, financial issues (less than 5% of the population is covered by the National Health Insurance Scheme), lack of awareness and the increasing expertise gap from the continued brain drain, despite the already low number of radiologists in the country. Given these obstacles, our long-term goal is to devise a rational basis for identifying mammographic risk categories so that this resource can be deployed cost-effectively. We propose to carry out a 5-year prospective mammographic study in Nigerian women and to use machine learning to develop a risk prediction algorithm, so that following a single screening mammogram, the appropriate frequency of subsequent mammograms can be determined. Our immediate goal is to examine the feasibility of performing such a study. To this end, we have initiated a cross-sectional pilot study that will recruit up to 2000 women in multiple urban centers in Nigeria. Objective: To explore feasibility of creating a digital mammography image database representative of the radiological features of the breast of Nigerian women. Objective: To explore feasibility of creating a database that can be used to inform a predictive mammographic risk model for breast cancer and identify the appropriate population that will benefit from breast cancer screening in Nigeria. Design This is a prospective, open-label, cross-sectional pilot study in 2000 eligible women in Nigeria and will include a single screening mammography examination. The mammography images will be read by the study radiologist. The Pilot phase will determine the feasibility and aid in the design of the of the Extension phase in which up to 20,000 participants will undergo yearly mammography examination for 5 years. Incident cases of breast cancer during the study period will permit the development of deep learning algorithms for predicting the risk of breast cancer in this population. Main Eligibility Criteria: Inclusion Criteria All consecutive women aged ≥40 years and &amp;lt; 65 years presenting for screening mammography at the participating institutions will be eligible. Signed study-specific written informed consent. Exclusion Criteria Complaint of a focal dominant lump or a bloody or clear nipple discharge. History of breast implants. Any woman who is pregnant or has reason to believe that she might be pregnant. Statistical Methods Analyses of continuous data will be summarized using descriptive statistics where the following parameters will be reported: Number of observations, Number of missing observations, Mean, Median, Standard deviation (SD), Minimum (Min), Maximum (Max) Categorical data will be presented with absolute and relative frequency (n and %). Present Accrual and Target accrual Two hundred ninety-six women have been enrolled and screening digital mammographic images using GE Digital mammography equipment have been obtained on all participants at 2 study sites in 2 cities in Southern Nigeria. The target accrual is 2000 participants over 12 months. Conclusion Our preliminary experience to date suggests the feasibility of proceeding with a larger prospective study to define mammographic risk groups in Nigerian women which will permit the rational allocation of resources for breast cancer prevention activities in this underserved population. Contact Information: Dr Toyin Shonukan at tshonukan@yemanjacancercare.org Citation Format: Oluwatoyin Shonukan, Hyelakumi Ibrahim, Esther Udoh, Wahab Egbeolu, Kanyinsola Oyeyinka, Jonas Haggstrom. A PROSPECTIVE PILOT STUDY OF MAMMOGRAPHIC PREDICTION OF WOMEN’S RISK (M-POWER) IN NIGERIA [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-20-01.

Mengeksplorasi Praktik Personal Digital Archiving (PDA) dalam Fenomena New Analog Photography di Instagram: Membangun Personal Branding, Pencarian Autentisitas dan Detoksifikasi Digital

This article discusses the phenomenon of the rise of analog photography called new analogphotography on Instagram. Apart from discussing the phenomenon of new analog photography, this article also discusses the practice of personal digital archiving (PDA), which is applied to this phenomenon. Social media is a platform for storing personal digital archives, which has been done by all humans in the internet era and electronic devices have become an integral part of their lives. The method used in writing articles uses a qualitative case study approach. The aim of this article is to find the causes of the new analog photography phenomenon, namely the millennial generation and Gen Z who are very active on social media and have personal branding awareness. The exploration carried out by the millennial generation and Gen Z who have an interest in photography to search for the authenticity of digital presence images and rituals using analog cameras can be categorized as digital detoxification. The limitation of this article is that the number of respondents obtained in quantity is not yet representative. It is hoped that future research will be able to gather more respondents with more varied demographics to be able to map the distribution of this phenomenon. This article explains that photography, which is a form of visual archive, is related to the study of social, cultural and technological topics. So that it provides a new perspective for developing the field of archives, especially special-form archives.

Abstract PO5-01-10: Evaluating a computer-aided platform for predicting recurrence and survival in an Oncotype Dx tested breast cancer cohort

Abstract Background: Oncotype DX Breast Recurrence ScoreÒ (ODXRS) is a widely used predictive measure of breast cancer recurrence risk and chemotherapy benefit for patients with estrogen receptor positive (ER+), human epidermal growth factor receptor-2 negative (HER2-) breast cancers. ODXRS testing incurs additional costs and delays. These drawbacks could be obviated by innovative computer-aided technologies within digital pathology workflows. Such technologies could be developed to perform a wide variety of diagnostic and prognostic tests on digital whole slide images (WSIs) of hematoxylin and eosin (H&amp;E)-stained tissue alone. This would save time, cost, and sample tissue whilst still providing critical prognostic information for patient management. Q-Plasia OncoReader Breast (QPORB) is a computer-aided solution which extracts information from H&amp;E WSIs to provide diagnostic and prognostic information for breast cancer specimens. Aims: The aim of this study was to identify whether the computer aided QPROB platform could successfully predict breast cancer recurrence from H&amp;E WSIs alone in line with recurrence predicted by ODXRS. Methods: H&amp;E slides corresponding to tissue blocks sent for ODXRS testing from primary breast cancer resection/excision specimens from St James’s University Hospital, Leeds, UK (n=137 cases, 1 slide per case) were collected, anonymized, and scanned at x20 magnification on an Aperio AT2 scanner. Relevant pathological and clinical data were collected from electronic pathology reports and patient records. These data included ODXRS and recurrence events. QPORB analyzed each case/slide and generated a digital biomarker profile from the H&amp;E images alone, combining statistical physics and tumor biology to quantify malignant cell cycle deformation. QPORB’s biomarker profile and ODXRS for each case was compared to disease-free survival (DFS) and overall survival (OS). Kaplan-Meier survival analyses were performed. Confounding factors (age, tumor grade, invasive tumor size and Charlson Comorbidity Index) were accounted for. Results: Results of three QPORB-generated indices - two putatively associated with overall cell cycle signature and one with G1 deformation, and their combined overall biomarker profile did not predict DFS in this cohort. However, with regards to OS, all three indices and the combined biomarker profile predicted OS over a median follow-up period of 5 years (P=0.044). 5% of WSIs (7/137) did not meet the criteria to be analyzed by QPORB and 4% (6/137) could not be analyzed by QPORB (attrition). While ODXRS similarly predicted OS but not DFS, the follow-up duration was suboptimal. Conclusion: While QPORB indices evaluated in this work, putatively associated with overall cell cycle signature and G1 deformation, did not predict breast cancer recurrence on a par with ODXRS, it has shown promising results as potential adjunct device to provide an estimate of OS for breast cancer patients eligible for ODXRS testing. Future work will expand on this observational cohort, blindly validate these findings, and extend the work to encompass all breast cancer molecular subtypes. Citation Format: Satabhisa Mukhopadhyay, Elizabeth Walsh, Rebecca Millican-Slater, Andrew Hanby, Joanne Stephenson, Tathagata Dasgupta, Nicolas Orsi. Evaluating a computer-aided platform for predicting recurrence and survival in an Oncotype Dx tested breast cancer cohort [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-01-10.

Improved image preprocessing method applicable to high-temperature digital image correlation measurement

Improved image preprocessing method applicable to high-temperature digital image correlation measurement

Curved Polymeric Sandwich Composites Subjected to Air Shock: An Experimental Investigation

Abstract Background The vulnerability of polymeric composite sandwich structures in marine applications to air explosions highlights a significant gap in our understanding of the dynamic behavior of the curved sandwich structures, which is essential for design improvements. Objective This study aims to explore the dynamic response and failure mechanisms of curved sandwich composite panels subjected to air-blast loading, providing insights into their structural integrity under such conditions. Methods Experiments were performed using laboratory-simulated air shocks generated by a shock tube, employing high-speed photography and digital image correlation to measure deflections on the back surface of the panels. The panels, made with PVC closed-cell foam cores of two densities (H45 and H130), were tested across three curved geometries (radii of 112 mm, 305 mm, and infinity) under various boundary conditions. Results Findings indicate an increase in deformation with a decreased radius of curvature under simple support conditions, a trend that reverses under arrested displacement conditions. Moreover, a reduced radius significantly enhances panel strength and resistance to interfacial damage, with the primary failure mode transitioning from core shear cracking to interfacial debonding as core density increases. Conclusions The study reveals that the radius of curvature, boundary conditions, and core density significantly affect curved sandwich panels’ dynamic response and performance. Panels with smaller radii and higher core densities exhibit increased strength, though boundary conditions introduce variable effects on deformation behavior.

Size effects of voids on the mechanical properties of 3D printed parts

AbstractAlthough the effects of additive manufacturing process parameters on the mechanical properties of 3D printed parts have been numerically investigated in the literature, less attention has been paid on the size effects of voids between deposited filaments. This study fills this gap by developing a methodology based on a combined finite element (FE) and design of experiment (DoE) technique. The development of FE methodology is based on micro-mechanical analysis of representative volume element (RVE) of 3D printed parts to predict the effective orthotropic properties. To account for the size effects of inter-bead voids, the RVE includes contribution of the multiple parameters of layer heights, layer widths and overlapping regions. To study the main and interaction effects of the above input parameters on the stiffness properties of 3D printed parts, a structured approach based on full factorial design is used. Although the size effects of voids on the constituents of elastic moduli of RVE were investigated, the main focus in the present work is to develop a regression model to predict the stiffness properties. The FE stress analysis of the RVE conducted in this study provides an insight about the potential failure modes such as delamination and filament debonding that may occur in load bearing 3D printed parts. For a case study, the results of FE-based homogenization technique in terms of stiffness properties are validated against the experimental data via three-point bending and Iosipescu shear tests which were conducted in conjunction with digital image correlation technique. The combined numerical and statistical approach proposed in this study provides a swift iterative design of 3D printed parts prior to the time-consuming computation modelling, contributing to reduce the number of tests and manufacturing costs.

Abstract PO5-04-05: Clinical validation of the concordance performance of ERBB2 status by single-cells genomics ERBB2 (HER2) amplification Assay in Circulating Tumor Cells (CTCs) from patients with matched HER2 status from metastatic tissue biopsies

Abstract Background Liquid biopsies are a non-invasive diagnostic approach for detecting CTCs that may provide clinically actionable information for treatment decisions for metastatic breast cancer (MBC) patients when a conventional biopsy is infeasible. Blood-based liquid biopsy has the potential advantage that it allows broad sampling of metastatic tumor clones because it is capable of and likely to obtain cellular and genomic contributions from more than one metastatic site. Here we report the concordance performance of HER2 positivity by single-cell CTC genomics ERBB2 (HER2) amplification in patients with matched HER2 status from metastatic tissue biopsies. Methods A prospective Clinical Experience Program for CTC ERBB2 (HER2) Assay across 25 different United States-based cancer centers, was used to recruit 128 MBC patients with documented standard-of-care metastatic tissue biopsies performed within the last 5 years (Days from tissue biopsy to liquid biopsy, Median: 19.5; interquartile range: 40.75) CTCs from blood collection at each respective clinical site were isolated from plasma, nucleated cells were plated, &amp; slides were bio-banked for Immunofluorescent staining &amp; subsequent imaging in a CAP CLIA lab. CTCs were identified using Epic Sciences digital imaging &amp; machine learning algorithms. Individual CTCs were sequenced for genomic quantification of chromosomal instability (LSTs) and ERBB2 (HER2) amplification. Results Within the clinical validation cohort of 128 MBC patients, 30% (38/128) were HER2-positive by standard pathologic criteria (IHC 3+ or IHC2+/ISH+) in metastatic tissue biopsies. Among the 64 out of 128 cases with reportable results by the CTC ERBB2 (HER2) amplification on chromosomally unstable CTCs, 31% (20/64) of patients had ERBB2 amplification. Concordance between CTC single cell genomics and available tissue results for HER2 positivity showed a sensitivity of 69% and specificity of 78%. We then addressed the effects of two known biological confounders to the use of tissue biopsy for HER2 status determination: 1) tissue biopsies performed on bone metastases, 2) tumor evolution driven by treatment pressure over time leading to potential HER2 conversion and tumor heterogeneity. Thus, we performed subgroup analysis including only validation set patients with tissue biopsies performed on non-bone tissues, which showed slightly improved concordance to the tissue comparator with a Sensitivity of 86%, and Specificity of 75%. Next, to exclude the possibility of tumor evolution we selected only patients with the HER2 status comparator performed on non-bone tissue biopsies contemporaneous (Days from tissue biopsy to liquid biopsy, Median: 14; interquartile range: 22) to the blood draw for the CTC ERBB2 (HER2) amplification assay. This showed dramatically improved concordance performance with a Sensitivity of 100%, and a Specificity of 75%. Conclusions Blood and tissue are two biologically distinct sample types where variations in HER2 and other biomarker results might be expected due to analytic, temporal and clinical differences. These data indicate that determination of HER2 status by single cell genomic ERBB2 (HER2) amplification assay can aid in the clinical assessment of ERBB2 status in patients with metastatic breast cancer (MBC) for whom tissue biopsy for HER2 status is not available, or infeasible. Additionally, these results provide evidence of the contribution of site differences (bone) of metastatic biopsies and also tumor evolution to discordance between the CTC ERBB2 (HER2) Assay and tumor biopsy. Citation Format: Giuseppe Di Caro, Ernest Lam, Megan Slade, Shuguang Huang, Rick Wenstrup, Lee Schwartzberg. Clinical validation of the concordance performance of ERBB2 status by single-cells genomics ERBB2 (HER2) amplification Assay in Circulating Tumor Cells (CTCs) from patients with matched HER2 status from metastatic tissue biopsies [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-04-05.

Abstract PO5-26-09: Digital image analysis and a novel set of cell line samples as aids in the development of a quantitative external quality assessment programme for Ki-67

Abstract Background and Aims Ki-67 is a well-established biomarker of proliferation in breast cancer (BC). However, its value in treatment decision making is hampered by a lack of analytical reproducibility. Regular participation in external quality assessment (EQA) substantially improves inter-laboratory concordance. Pre-requisites in establishing a fit-for-purpose EQA for Ki-67 are a well-validated testing substrate and a reproducible method of assessing Ki-67 scores in participants’ submitted materials. We report here on the results of work using cell line controls analysed by digital image analysis (DIA) as a first step towards providing such an EQA. Materials and Methods A formalin-fixed paraffin embedded (FFPE) cell line microarray (CLMA) was designed and produced in conjunction with Array Sciences LLC (Sausalito, USA). It was comprised of cores taken from a pure population of Sf9 caterpillar cells, which have been shown to be completely unreactive with most commercial antibodies to human Ki-67, together with cores of Sf9 cells mixed with four different human BC cell lines. These were BT-20 (85% BC cells), ZR-75-1 (75%), BT-474 (65%) and BT-483 (55%). Sections from the CLMA were mounted onto glass microscope slides together with sections from a FFPE tonsil sample and two BC samples; one BC showed high (~30%, BC-high) and the second, low proliferation (~5%, BC-low). In both BC samples proliferating cancer cells were distributed homogeneously throughout the the block. Unstained sections were distributed to laboratories participating in the Scheme’s Ki-67 BC programme; after routine IHC-staining for Ki-67 returned slides were centrally visually assessed for stain quality and subjected to DIA using an in-house application developed on Visiopharm software (Visiopharm A/S, Hoersholm, Denmark). Results Slides were returned by 37 laboratories. Analysis of Ki-67 scores obtained on the Sf9 core identified two distinct groups. The first (n = 27) were negative or showed low Ki-67 scores (mean = 1.1%, 95% CIs: 0.2-1.9%), the second (n = 10) displayed a step-change in scores (mean = 49.9%, 95% CIs: 33.2-66.7%); the means of the two groups were significantly different (P&amp;lt; 0.0001). When Ki-67 scores for each of the tissue samples were dichotomized into similar groups, the means of those groups differed significantly for the two BC samples (P&amp;lt; 0.001), but not for tonsil. Quality scores generated by visual assessment did not differ significantly between the two groups. However, when slides bearing Sf9 cores demonstrating aberrant Ki-67 scores were visually examined nuclear staining was clearly visible, and non-specific nuclear staining could also be identified in the matched BC tissue samples, but not in the tonsil sections. Correlation of Ki-67 scores between the four BC cell line cores and each of the BC tissue samples was examined using Pearson’s correlation statistics. The r statistic range was 0.57-0.71 in comparisons between BC cell line cores and the BC-high sample, and 0.53-0.70 for those with BC-low; in each case BT-483 showed the highest correlation score and BT-20 the lowest. A similar analysis was undertaken between the tonsil and the two BC tissues. The r statistic for correlation between tonsil and BC-high scores was 0.72; it was 0.64 for those between tonsil and BC-low. Conclusions By using a pure population of Sf9 cells we have developed a sensitive indicator of non-specific nuclear staining in IHC preparations stained for Ki-67 which identifies the presence of the artefact quantifiably. Cores made from Sf9/BC cell line mixtures (especially BT-483) produce Ki-67 scores which correlate with those obtained in breast cancer samples at a similar level to those achieved between tonsil and BC samples; this is true for both high and the low proliferation ranges. Cell line mixtures can be adjusted to show Ki-67 scores in the clinically relevant ranges, and they do not show the inherent biological variations seen in tissue controls such as tonsil. Citation Format: Andrew Dodson, Fitim Berisha, Dawn Wilkingson, Lila Zabaglo, Suzanne Parry. Digital image analysis and a novel set of cell line samples as aids in the development of a quantitative external quality assessment programme for Ki-67 [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-26-09.

An intelligent lithology recognition system for continental shale by using digital coring images and convolutional neural networks

Lithology identification is a critical component of reservoir evaluation and hydrocarbon development. However, the continental shale rocks, characterized by complex lithologies and developed laminations, present new challenges to traditional lithology identification techniques. In this study, we develop a centimeter-level intelligent identification system for continental shale lithology, leveraging digital image preprocessing and deep learning methods. Specifically, we construct a dataset from 400 meters of drilling cores, encompassing 24 types of lithology and 152 lithological transitions. The downsampling and oversampling methods are employed to balance the dataset. The digital image denoising and restoration techniques are then utilized to enhance rock image features. Different neural network models are examined to obtain the optimum predictive results. The results indicate that data balancing improves the TOP1 and TOP5 accuracies by 10% and 5%, respectively. Gaussian low-pass filter denoising, in comparison to other methods, increases the TOP1 and TOP5 accuracies by 2%, establishing it as the superior denoising technique for lithology recognition. Considering both computational efficiency and prediction accuracy, EfficientNet emerges as the most effective model for continental shale image recognition. The optimized model attains up to 60% TOP1 and 95% TOP5 across 22 categories in drilling core samples. This study delves into the intelligent identification of macroscopic drilling core images of continental shale, and the findings can offer technical support for the interpretation and evaluation of continental shale reservoirs.

Joint Metric Learning-Based Class-Specific Representation for Image Set Classification.

With the rapid advances in digital imaging and communication technologies, recently image set classification has attracted significant attention and has been widely used in many real-world scenarios. As an effective technology, the class-specific representation theory-based methods have demonstrated their superior performances. However, this type of methods either only uses one gallery set to measure the gallery-to-probe set distance or ignores the inner connection between different metrics, leading to the learned distance metric lacking robustness, and is sensitive to the size of image sets. In this article, we propose a novel joint metric learning-based class-specific representation framework (JMLC), which can jointly learn the related and unrelated metrics. By iteratively modeling probe set and related or unrelated gallery sets as affine hull, we reconstruct this hull sparsely or collaboratively over another image set. With the obtained representation coefficients, the combined metric between the query set and the gallery set can then be calculated. In addition, we also derive the kernel extension of JMLC and propose two new unrelated set constituting strategies. Specifically, kernelized JMLC (KJMLC) embeds the gallery sets and probe sets into the high-dimensional Hilbert space, and in the kernel space, the data become approximately linear separable. Extensive experiments on seven benchmark databases show the superiority of the proposed methods to the state-of-the-art image set classifiers.

Lightweight JPEG image steganalysis using dilated blind-spot network

The primary goal of image steganalysis is to detect subtle steganographic signals hidden within digital images. An essential aspect of digital image steganalysis involves the extraction of meaningful steganographic signal features. However, existing JPEG steganalysis networks have not delved deep enough into the research on extracting steganographic noise signals, especially in the preprocessing stage of the model. In this paper, we propose a novel approach called the Dilated Blind-Spot Steganalysis Network (DBS2Net), specifically designed for JPEG steganalysis. DBS2Net incorporates the dilated blind-spot convolutional network into the preprocessing stage to extract noise residuals, thereby enhancing detection accuracy. The blind-spot network estimates central outputs based on adjacent regions to effectively suppress semantic information and uses dilated convolutions to expand the receptive field. Additionally, we design the transition block to further enhance noise signals by mapping noise residuals extracted by the blind-spot network. In feature extraction, depth-wise separable convolutions are used to minimize the parameter count, while dual-view pooling layers compress features, resulting in improved detection performance. Experimental results validate the efficacy of this approach, and comprehensive analyses of JPEG steganographic noise support its success. In particular, DBS2Net outperforms state-of-the-art steganalysis methods, achieving superior detection accuracy with minimal model complexity.

Prediction and modeling of tie-dye polyester fabric's texture and color feature in supercritical carbon dioxide

To mitigate the water-intensive challenges of tie-dye clothing production and address the absence of objective evaluation for finished products, we have explored the correlation between tie-dye techniques in supercritical carbon dioxide (SC-CO2) and image patterns. Employing digital image processing, we extracted the average HSV three-component value of the valid tie-dye area, the proportion of incompletely dyed area in the HSV color space, and the initial three texture features of the digital tie-dye image using the Tamura method. We scrutinized five factors in the supercritical CO2 dyeing process: rotational speed, dyeing temperature, dyeing pressure, dye concentration, and dyeing time. The single-factor climbing test revealed that temperature and pressure are the predominant factors influencing tie-dyeing performance. We propose a correlation between the texture parameters of tie-dyeing and the swelling effect of polyester fabric in a supercritical CO2 environment. Subsequently, we conducted two-factor tests on temperature and pressure to investigate their specific effects on the parameters of supercritical polyester tie-dyeing. The results indicated significant changes in regularity, saturation, and the proportion of undyed area with variations in dyeing temperature and pressure. Building upon these findings, we developed an artificial neural network for predicting tie-dye dyeing temperature and pressure in supercritical CO2, utilizing features regularity, saturation, and proportion of undyed area as inputs. In the 105st round, experimental results demonstrated an accuracy of 0.99445, substantiating the efficacy of tie-dye pattern features in supercritical CO2 for technique forecasting.

Portable spectroscopy, digital imaging colorimetry and multivariate statistical tools in contaminant identification: A case study of mint (Mentha) and basil (Ocimum basilicum)

The advent of portable Fourier-Transform Infrared (FTIR) and Raman spectrometers has revolutionized analysis capabilities, presenting the possibility of on-site contaminant identification without the need for specialized laboratory settings. Compared to laboratory instrumentation, portable spectroscopy is more prone to noise, and appropriate spectral processing procedures need to be established. This paper introduces a comprehensive methodology that integrates acquisition techniques, spectral analysis, and mathematical tools necessary for utilizing handheld spectrometers to diagnose plant contamination. It focuses on determining the efficacy of handheld FTIR, Raman spectroscopy, and digital imaging for detecting contaminants in two food plants, Basil (Ocimum basilicum) and Mint (Mentha). The study examines the impact of three pollutants: iron (II) sulphate (FeSO4), zinc (II) sulphate (ZnSO4), and copper (II) sulphate (CuSO4), on these plants, but also the necessary amount of measurements to spot the pollutants' effects. Measurements were conducted at the start, after 24 hours, and after 48 hours of exposure, on both fresh and dried plant leaves, as well as in solution. Spectral effects of each of the pollutants were identified with the use of multivariate statistical process control techniques. With the help of the developed methodologies, researchers can identify in-situ contaminant effects, exposure times and run diagnostics directly on the leaf both in alive and dried plants.

Characterizing damage evolution in fiber reinforced composites using in-situ X-ray computed tomography, deep machine learning and digital volume correlation (DVC)

In-situ X-ray computed tomography (CT) techniques enable to examine microstructural evolution of composite materials continuously and nondestructively under loading for better understanding the initiation and propagation of microscopic damages. This study explores the tensile damage behavior of notched woven carbon/epoxy composites under successive loading process. Since the image contrast of different composite material phases is normally low and conventional image segmentation techniques are hard to identify microstructures and defects in different directions distinctively, a U-Net image segmentation model based on deep machine learning algorithms is employed to characterize microstructures and material damages precisely and consistently. Digital volume correlation (DVC) approach is proposed here to measure the three-dimensional (3D) deformation fields of carbon/epoxy composites at different loading steps, and the DVC results are used to predict location of damage initiation and propagation. It is found that the damage modes of notched carbon/epoxy composites mainly include fiber breakage, longitudinal cracks in warp fiber tows, and transverse cracks in weft fiber tows. The individual fiber breakage occurs in warp fiber tows at the intermediate loading level, and longitudinal and transverse cracks appear in further loading levels. Longitudinal cracks and fiber breakage events take place in a region tangent to the notch tip and exhibit a strong interaction. The development of longitudinal cracks promotes the initiation and propagation of transverse cracks. In addition, the strain concentration regions in 3D deformation fields match well with the microscopic cracks segmented by the U-Net deep learning model. This study demonstrated that the integration of X-ray computed tomography, digital volume correlation and image semantic segmentation enables to identify damage initiation, evolution, extent and mechanism.

LUNet: deep learning for the segmentation of arterioles and venules in high resolution fundus images

Objective. This study aims to automate the segmentation of retinal arterioles and venules (A/V) from digital fundus images (DFI), as changes in the spatial distribution of retinal microvasculature are indicative of cardiovascular diseases, positioning the eyes as windows to cardiovascular health. Approach. We utilized active learning to create a new DFI dataset with 240 crowd-sourced manual A/V segmentations performed by 15 medical students and reviewed by an ophthalmologist. We then developed LUNet, a novel deep learning architecture optimized for high-resolution A/V segmentation. The LUNet model features a double dilated convolutional block to widen the receptive field and reduce parameter count, alongside a high-resolution tail to refine segmentation details. A custom loss function was designed to prioritize the continuity of blood vessel segmentation. Main Results. LUNet significantly outperformed three benchmark A/V segmentation algorithms both on a local test set and on four external test sets that simulated variations in ethnicity, comorbidities and annotators. Significance. The release of the new datasets and the LUNet model (www.aimlab-technion.com/lirot-ai) provides a valuable resource for the advancement of retinal microvasculature analysis. The improvements in A/V segmentation accuracy highlight LUNet's potential as a robust tool for diagnosing and understanding cardiovascular diseases through retinal imaging.

Deep learning system for screening AIDS-related cytomegalovirus retinitis with ultra-wide-field fundus images

BackgroundOphthalmological screening for cytomegalovirus retinitis (CMVR) for HIV/AIDS patients is important to prevent lifelong blindness. Previous studies have shown good properties of automated CMVR screening using digital fundus images. However, the application of a deep learning (DL) system to CMVR with ultra-wide-field (UWF) fundus images has not been studied, and the feasibility and efficiency of this method are uncertain. MethodsIn this study, we developed, internally validated, externally validated, and prospectively validated a DL system to detect AIDS-related from UWF fundus images from different clinical datasets. We independently used the InceptionResnetV2 network to develop and internally validate a DL system for identifying active CMVR, inactive CMVR, and non-CMVR in 6960 UWF fundus images from 862 AIDS patients and validated the system in a prospective and an external validation data set using the area under the curve (AUC), accuracy, sensitivity, and specificity. A heat map identified the most important area (lesions) used by the DL system for differentiating CMVR. ResultsThe DL system showed AUCs of 0.945 (95 % confidence interval [CI]: 0.929, 0.962), 0.964 (95 % CI: 0.870, 0.999) and 0.968 (95 % CI: 0.860, 1.000) for detecting active CMVR from non-CMVR and 0.923 (95 % CI: 0.908, 0.938), 0.902 (0.857, 0.948) and 0.884 (0.851, 0.917) for detecting active CMVR from non-CMVR in the internal cross-validation, external validation, and prospective validation, respectively. Deep learning performed promisingly in screening CMVR. It also showed the ability to differentiate active CMVR from non-CMVR and inactive CMVR as well as to identify active CMVR and inactive CMVR from non-CMVR (all AUCs in the three independent data sets >0.900). The heat maps successfully highlighted lesion locations. ConclusionsOur UWF fundus image-based DL system showed reliable performance for screening AIDS-related CMVR showing its potential for screening CMVR in HIV/AIDS patients, especially in the absence of ophthalmic resources.

VAULT: vault accuracy using deep learning technology: new image-based artificial intelligence model for predicting implantable collamer lens postoperative vault.

To develop an accurate deep learning model to predict postoperative vault of phakic implantable collamer lenses (ICLs). Parkhurst NuVision LASIK Eye Surgery, San Antonio, Texas. Retrospective machine learning study. 437 eyes of 221 consecutive patients who underwent ICL implantation were included. A neural network was trained on preoperative very high-frequency digital ultrasound images, patient demographics, and postoperative vault. 3059 images from 437 eyes of 221 patients were used to train the algorithm on individual ICL sizes. The 13.7 mm size was excluded because of insufficient data. A mean absolute error of 66.3 μm, 103 μm, and 91.8 μm were achieved with 100%, 99.0%, and 96.6% of predictions within 500 μm for the 12.1 mm, 12.6 mm, and 13.2 mm sizes, respectively. This deep learning model achieved a high level of accuracy of predicting postoperative ICL vault with the overwhelming majority of predictions successfully within a clinically acceptable margin of vault.

In Vivo Biomechanical Dynamic Simulation of a Healthy Knee During the Single-leg Lunge and Its In Vitro Experiment Validation

Biomechanical modelling of the knee during motion is a pivotal component in disease treatment, implant designs, and rehabilitation strategies. Historically, dynamic simulations of the knee have been scant. This study uniquely integrates a dual fluoroscopic imaging system (DFIS) to investigate the in vivo dynamic behavior of the meniscus during functional activities using a finite element (FE) model. The model was subsequently validated through in vitro experiments. Motion capture of a single-leg lunge was executed by DFIS. The motion model was reconstructed using 2D-to-3D registration in conjunction with computed tomography (CT) scans. Both CT and magnetic resonance imaging (MRI) data facilitated the development of the knee FE model. In vivo knee displacements and rotations were utilized as driving conditions for the FE model. Moreover, a 3D-printed model, accompanied with digital imaging correlation (DIC), was used to evaluate the accuracy of the FE model. To a better inner view of knees during the DIC analysis, tibia and femur were crafted by transparent resin. The availability of the FE model was guaranteed by the similar strain distribution of the DIC and FE simulation. Subsequent modeling revealed that the compressive stress distribution between the medial and lateral menisci was balanced in the standing posture. As the flexion angle increased, the medial meniscus bore the primary compressive load, with peak stresses occurring between 60 and 80 degrees of flexion. The simulation of a healthy knee provides a critical theoretical foundation for addressing knee pathologies and advancing prosthetic designs.