Automated Quantification Method Research Articles

An automatic measurement method for the response of Caenorhabditis elegans to chemicals.

Caenorhabditis elegans is a widely used model animal. Chemotaxis assay is one of the experiments that study the effects of different chemicals on nematodes. It is mainly used to study the effects of different chemicals on the perception behavior of nematodes. By conducting this experiment, not only can the neurotoxicity of chemicals be reflected, but also the impact of chemicals on physiological functions regulated by the nervous system, such as nematode feeding behavior and basic motor ability. The experiment of detecting the response of nematode to chemicals is also a common method of chemical toxicity testing based on nematode models. In the analysis of worm tendency behavior, manual operations are generally used. Manually processing a large number of worms under a microscope is very time-consuming and labor-intensive. The current quantitative methods for nematode chemotaxis experiments are not only time-consuming and labor-intensive, but also biased in experimental results due to differences in judgment standards among experimenters. The automatic and efficient quantification method for nematode chemotaxis experiments is a very important technical difficulty in the field of nematode experiments. Here, we have designed an automatic quantification method for nematode chemotaxis experiments by incorporating image acquisition and processing techniques into the nematode experiment. The experimental results show that the Pearson correlation coefficient between manual and automatic counting results is 0.978. This proves the effectiveness of our method. Applying the automatic measurement method to replace manual counting by the experimenter can improve work efficiency, and reduce errors in human counting operations.

Artificial intelligence-based quantitative coronary angiography of major vessels using deep-learning

BackgroundQuantitative coronary angiography (QCA) offers objective and reproducible measures of coronary lesions. However, significant inter- and intra-observer variability and time-consuming processes hinder the practical application of on-site QCA in the current clinical setting. This study proposes a novel method for artificial intelligence-based QCA (AI-QCA) analysis of the major vessels and evaluates its performance. MethodsAI-QCA was developed using three deep-learning models trained on 7658 angiographic images from 3129 patients for the precise delineation of lumen boundaries. An automated quantification method, employing refined matching for accurate diameter calculation and iterative updates of diameter trend lines, was embedded in the AI-QCA. A separate dataset of 676 coronary angiography images from 370 patients was retrospectively analyzed to compare AI-QCA with manual QCA performed by expert analysts. A match was considered between manual and AI-QCA lesions when the minimum lumen diameter (MLD) location identified manually coincided with the location identified by AI-QCA. Matched lesions were evaluated in terms of diameter stenosis (DS), MLD, reference lumen diameter (RLD), and lesion length (LL). ResultsAI-QCA exhibited a sensitivity of 89% in lesion detection and strong correlations with manual QCA for DS, MLD, RLD, and LL. Among 995 matched lesions, most cases (892 cases, 80%) exhibited DS differences ≤10%. Multiple lesions of the major vessels were accurately identified and quantitatively analyzed without manual corrections. ConclusionAI-QCA demonstrates promise as an automated tool for analysis in coronary angiography, offering potential advantages for the quantitative assessment of coronary lesions and clinical decision-making.

Development of a cancer metastasis-on-chip assay for high throughput drug screening.

Metastasis is the cause of most triple-negative breast cancer deaths, yet anti-metastatic therapeutics remain limited. To develop new therapeutics to prevent metastasis, pathophysiologically relevant assays that recapitulate tumor microenvironment is essential for disease modeling and drug discovery. Here, we have developed a microfluidic metastasis-on-chip assay of the early stages of cancer metastasis integrated with the triple-negative breast cancer cell line (MDA-MB-231), stromal fibroblasts and a perfused microvessel. High-content imaging with automated quantification methods was optimized to assess the tumor cell invasion and intravasation within the model. Cell invasion and intravasation were enhanced when fibroblasts co-cultured with a breast cancer cell line (MDA-MB-231). However, the non-invasive breast cancer cell line, MCF7, remained non-invasive in our model, even in the presence of fibroblasts. High-content screening of a targeted anti-cancer therapy drug library was conducted to evaluate the drug response sensitivity of the optimized model. Through this screening, we identified 30 compounds that reduced the tumor intravasation by 60% compared to controls. Multi-parametric phenotypic analysis was applied by combining the data from the metastasis-on-chip, cell proliferation and 2D cell migration screens, revealing that the drug library was clustered into eight distinct groups with similar drug responses. Notably, MEK inhibitors were enriched in cluster cell invasion and intravasation. In contrast, drugs with molecular targets: ABL, KIT, PDGF, SRC, and VEGFR were enriched in the drug clusters showing a strong effect on tumor cell intravasation with less impact on cell invasion or cell proliferation, of which, Imatinib, a multi-kinase inhibitor targeting BCR-ABL/PDGFR/KIT. Further experimental analysis showed that Imatinib enhanced endothelial barrier stability as measured by trans-endothelial electrical resistance and significantly reduced the trans-endothelial invasion activity of tumor cells. Our findings demonstrate the potential of our metastasis-on-chip assay as a powerful tool for studying cancer metastasis biology, drug discovery aims, and assessing drug responses, offering prospects for personalized anti-metastatic therapies for triple-negative breast cancer patients.

A transformer-based deep learning method for automatic pixel-level crack detection and feature quantification

Purpose Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement performance.Design/methodology/approach This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient productivity.Findings In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 × 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher reliability.Originality/value The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and evaluation metrics, which can provide solutions with potentially serving as the basis for an automated, cost-effective pavement condition assessment scheme.

Extended automated quantification algorithm (AQuA) for targeted 1H NMR metabolomics of highly complex samples: application to plant root exudates

IntroductionThe Automated Quantification Algorithm (AQuA) is a rapid and efficient method for targeted NMR-based metabolomics, currently optimised for blood plasma. AQuA quantifies metabolites from 1D-1H NMR spectra based on the height of only one signal per metabolite, which minimises the computational time and workload of the method without compromising the quantification accuracy.ObjectivesTo develop a fast and computationally efficient extension of AQuA for quantification of selected metabolites in highly complex samples, with minimal prior sample preparation. In particular, the method should be capable of handling interferences caused by broad background signals.MethodsAn automatic baseline correction function was combined with AQuA into an automated workflow, the extended AQuA, for quantification of metabolites in plant root exudate NMR spectra that contained broad background signals and baseline distortions. The approach was evaluated using simulations as well as a spike-in experiment in which known metabolite amounts were added to a complex sample matrix.ResultsThe extended AQuA enables accurate quantification of metabolites in 1D-1H NMR spectra with varying complexity. The method is very fast (< 1 s per spectrum) and can be fully automated.ConclusionsThe extended AQuA is an automated quantification method intended for 1D-1H NMR spectra containing broad background signals and baseline distortions. Although the method was developed for plant root exudates, it should be readily applicable to any NMR spectra displaying similar issues as it is purely computational and applied to NMR spectra post-acquisition.

Microporous Polylactic Acid Scaffolds Enable Fluorescence-Based Perfusion Imaging of Intrinsic In Vivo Vascularization.

In vivo tissue engineering (TE) techniques like the AV loop model provide an isolated and well-defined microenvironment to study angiogenesis-related cell interactions. Functional visualization of the microvascular network within these artificial tissue constructs is crucial for the fundamental understanding of vessel network formation and to identify the underlying key regulatory mechanisms. To facilitate microvascular tracking advanced fluorescence imaging techniques are required. We studied the suitability of microporous polylactic acid (PLA) scaffolds with known low autofluorescence to form axial vascularized tissue constructs in the AV loop model and to validate these scaffolds for fluorescence-based perfusion imaging. Compared to commonly used collagen elastin (CE) scaffolds, the total number of vessels and cells in PLA scaffolds was lower. In detail, CE-based constructs exhibited significantly higher vessel numbers on day 14 and 28 (d14: 316 ± 53; d28: 610 ± 74) compared to the respective time points in PLA-based constructs (d14: 144 ± 18; d28: 327 ± 34; each p < 0.05). Analogously, cell counts in CE scaffolds were higher compared to corresponding PLA constructs (d14: 7661.25 ± 505.93 and 5804.04 ± 716.59; d28: 11211.75 + 1278.97 and 6045.71 ± 572.72, p < 0.05). CE scaffolds showed significantly higher vessel densities in proximity to the main vessel axis compared to PLA scaffolds (200-400 µm and 600-800 µm on day 14; 400-1000 µm and 1400-1600 µm on day 28). CE scaffolds had significantly higher cell counts on day 14 at distances from 800 to 2000 µm and at distances from 400 to 1600 µm on day 28. While the total number of vessels and cells in PLA scaffolds were lower, both scaffold types were ideally suited for axial vascularization techniques. The intravascular perfusion of PLA-based constructs with fluorescence dye MHI148-PEI demonstrated dye specificity against vascular walls of low- and high-order branches as well as capillaries and facilitated the fluorescence-based visualization of microcirculatory networks. Fluorophore tracking may contribute to the development of automated quantification methods after 3D reconstruction and image segmentation. These technologies may facilitate the characterization of key regulators within specific subdomains and add to the current understanding of vessel formation in axially vascularized tissue constructs.

Automatic 3D Postoperative Evaluation of Complex Orthopaedic Interventions.

In clinical practice, image-based postoperative evaluation is still performed without state-of-the-art computer methods, as these are not sufficiently automated. In this study we propose a fully automatic 3D postoperative outcome quantification method for the relevant steps of orthopaedic interventions on the example of Periacetabular Osteotomy of Ganz (PAO). A typical orthopaedic intervention involves cutting bone, anatomy manipulation and repositioning as well as implant placement. Our method includes a segmentation based deep learning approach for detection and quantification of the cuts. Furthermore, anatomy repositioning was quantified through a multi-step registration method, which entailed a coarse alignment of the pre- and postoperative CT images followed by a fine fragment alignment of the repositioned anatomy. Implant (i.e., screw) position was identified by 3D Hough transform for line detection combined with fast voxel traversal based on ray tracing. The feasibility of our approach was investigated on 27 interventions and compared against manually performed 3D outcome evaluations. The results show that our method can accurately assess the quality and accuracy of the surgery. Our evaluation of the fragment repositioning showed a cumulative error for the coarse and fine alignment of 2.1 mm. Our evaluation of screw placement accuracy resulted in a distance error of 1.32 mm for screw head location and an angular deviation of 1.1° for screw axis. As a next step we will explore generalisation capabilities by applying the method to different interventions.

Automatic quantification and assessment of grouped pig movement using the XGBoost and YOLOv5s models

The movements of grouped pigs reflect the health of the pigs to a certain extent. Monitoring pig movement is dependent upon daily patrols and the experience and judgment of breeders. This study innovatively proposes an automatic quantification and assessment method for the movements of grouped pigs using video data from the top view of a pigpen. The YOLOv5s model was used to detect the positions of grouped pigs. The mean-shift algorithm was used to identify the movement center of grouped pigs, and then the movement center was tracked, quantified, and counted. Based on the quantized values, we divided the movements of pigs into three levels. The extreme gradient boosting (XGBoost) model was used to assess the movements of grouped pigs. In the model training and position detection experiments, the P-R curve value of the YOLOv5s position detection model was 0.985, the precision was 0.97, the recall was 0.96, and the mean average precision was 0.5 (mAP_0.5) = 0.99. The XGBoost classification model was used to assess and classify the movement of grouped pigs, and the overall accuracy was 97.4%. Using this method to investigate movement over time, we found that the pigs were more active from 08:00 to 11:00 and 16:00 to 19:00 and that the movement center of grouped pigs changed over time. These results were consistent with the actual observations of grouped pigs, indicating that it is feasible to apply the method proposed in this study to large-scale farming operations.

Automatic quantification method of eosinophilic degranulation in tissues: Application for the study of eosinophilic disorders.

Automatic quantification method of eosinophilic degranulation in tissues: Application for the study of eosinophilic disorders.

Automatic quantification method of eosinophilic degranulation in tissues: application for the study of eosinophilic disorders

Automatic quantification method of eosinophilic degranulation in tissues: application for the study of eosinophilic disorders

Quantification of liver-Lung shunt fraction on 3D SPECT/CT images for selective internal radiation therapy of liver cancer using CNN-based segmentations and non-rigid registration

Purpose: Selective internal radiation therapy (SIRT) has been proven to be an effective treatment for hepatocellular carcinoma (HCC) patients. In clinical practice, the treatment planning for SIRT using 90Y microspheres requires estimation of the liver-lung shunt fraction (LSF) to avoid radiation pneumonitis. Currently, the manual segmentation method to draw a region of interest (ROI) of the liver and lung in 2D planar imaging of 99mTc-MAA and 3D SPECT/CT images is inconvenient, time-consuming and observer-dependent. In this study, we propose and evaluate a nearly automatic method for LSF quantification using 3D SPECT/CT images, offering improved performance compared with the current manual segmentation method.Methods: We retrospectively acquired 3D SPECT with non-contrast-enhanced CT images (nCECT) of 60 HCC patients from a SPECT/CT scanning machine, along with the corresponding diagnostic contrast-enhanced CT images (CECT). Our approach for LSF quantification is to use CNN-based methods for liver and lung segmentations in the nCECT image. We first apply 3D ResUnet to coarsely segment the liver. If the liver segmentation contains a large error, we dilate the coarse liver segmentation into the liver mask as a ROI in the nCECT image. Subsequently, non-rigid registration is applied to deform the liver in the CECT image to fit that obtained in the nCECT image. The final liver segmentation is obtained by segmenting the liver in the deformed CECT image using nnU-Net. In addition, the lung segmentations are obtained using 2D ResUnet. Finally, LSF quantitation is performed based on the number of counts in the SPECT image inside the segmentations.Evaluations and Results: To evaluate the liver segmentation accuracy, we used Dice similarity coefficient (DSC), asymmetric surface distance (ASSD), and max surface distance (MSD) and compared the proposed method to five well-known CNN-based methods for liver segmentation. Furthermore, the LSF error obtained by the proposed method was compared to a state-of-the-art method, modified Deepmedic, and the LSF quantifications obtained by manual segmentation. The results show that the proposed method achieved a DSC score for the liver segmentation that is comparable to other state-of-the-art methods, with an average of 0.93, and the highest consistency in segmentation accuracy, yielding a standard deviation of the DSC score of 0.01. The proposed method also obtains the lowest ASSD and MSD scores on average (2.6 mm and 31.5 mm, respectively). Moreover, for the proposed method, a median LSF error of 0.14% is obtained, which is a statically significant improvement to the state-of-the-art-method (p=0.004), and is much smaller than the median error in LSF manual determination by the medical experts using 2D planar image (1.74% and p<0.001).Conclusions: A method for LSF quantification using 3D SPECT/CT images based on CNNs and non-rigid registration was proposed, evaluated and compared to state-of-the-art techniques. The proposed method can quantitatively determine the LSF with high accuracy and has the potential to be applied in clinical practice.

Fully Automatic Method for Reliable Spinal Cord Compartment Segmentation in Multiple Sclerosis.

Fully automatic quantification methods of spinal cord compartments are needed to study pathologic changes of the spinal cord GM and WM in MS in vivo. We propose a novel method for automatic spinal cord compartment segmentation (SCORE) in patients with MS. The cervical spinal cords of 24 patients with MS and 24 sex- and age-matched healthy controls were scanned on a 3T MR imaging system, including an averaged magnetization inversion recovery acquisition sequence. Three experienced raters manually segmented the spinal cord GM and WM, anterior and posterior horns, gray commissure, and MS lesions. Subsequently, manual segmentations were used to train neural segmentation networks of spinal cord compartments with multidimensional gated recurrent units in a 3-fold cross-validation fashion. Total intracranial volumes were quantified using FreeSurfer. The intra- and intersession reproducibility of SCORE was high in all spinal cord compartments (eg, mean relative SD of GM and WM: ≤ 3.50% and ≤1.47%, respectively) and was better than manual segmentations (all P < .001). The accuracy of SCORE compared with manual segmentations was excellent, both in healthy controls and in patients with MS (Dice similarity coefficients of GM and WM: ≥ 0.84 and ≥0.92, respectively). Patients with MS had lower total WM areas (P < .05), and total anterior horn areas (P < .01 respectively), as measured with SCORE. We demonstrate a novel, reliable quantification method for spinal cord tissue segmentation in healthy controls and patients with MS and other neurologic disorders affecting the spinal cord. Patients with MS have reduced areas in specific spinal cord tissue compartments, which may be used as MS biomarkers.

Multilesion Segmentations in Patients with Intracerebral Hemorrhage: Reliability of ICH, IVH and PHE Masks.

Background and Purpose: Fully automated methods for segmentation and volume quantification of intraparenchymal hemorrhage (ICH), intraventricular hemorrhage extension (IVH), and perihematomal edema (PHE) are gaining increasing interest. Yet, reliabilities demonstrate considerable variances amongst each other. Our aim was therefore to evaluate both the intra- and interrater reliability of ICH, IVH and PHE on ground-truth segmentation masks. Methods: Patients with primary spontaneous ICH were retrospectively included from a German tertiary stroke center (Charité Berlin; January 2016−June 2020). Baseline and follow-up non-contrast Computed Tomography (NCCT) scans were analyzed for ICH, IVH, and PHE volume quantification by two radiology residents. Raters were blinded to all demographic and outcome data. Inter- and intrarater agreements were determined by calculating the Intraclass Correlation Coefficient (ICC) for a randomly selected set of patients with ICH, IVH, and PHE. Results: 100 out of 670 patients were included in the analysis. Interrater agreements ranged from an ICC of 0.998 for ICH (95% CI [0.993; 0.997]), to an ICC of 0.979 for IVH (95% CI [0.984; 0.993]), and an ICC of 0.886 for PHE (95% CI [0.760; 0.938]), all p-values < 0.001. Intrarater agreements ranged from an ICC of 0.997 for ICH (95% CI [0.996; 0.998]), to an ICC of 0.995 for IVH (95% CI [0.992; 0.996]), and an ICC of 0.980 for PHE (95% CI [0.971; 0.987]), all p-values < 0.001. Conclusion Manual segmentations of ICH, IVH, and PHE demonstrate good-to-excellent inter- and intrarater reliabilities, with the highest agreement for ICH and IVH and lowest for PHE. Therefore, the degree of variances reported in fully automated quantification methods might be related amongst others to variances in ground-truth masks.

Construction of an Automatic Quantification Method for Bone Marrow Cellularity Using Image Analysis Software.

Although rapid, the evaluation of bone marrow (BM) cellularity is semi-quantitative and largely dependent upon visual estimates. We aimed to construct an automatic quantification method using image analysis software. We used hematoxylin and eosin (HE)-stained specimens of BM biopsies and clots from patients who underwent BM examination at Tottori University Hospital from 2020 to 2022. We compared image analysis (Methods A, B, and C) with visual estimates in pathology reports of 91 HE specimens in 54 cases (29 males, 25 females), including 38 biopsy and 53 clot specimens. Cellularity was visually scored as hypocellular (n = 17), normocellular (n = 44), or hypercellular (n = 30). Compared with the visual estimates, intraclass correlation coefficients for Methods A, B, and C were 0.80, 0.85, and 0.88, respectively. The most appropriate values were obtained with Method C which detected both non-fatty and cell nuclear areas.

Morphological characterization of chip segmentation in Ti-6Al-7Nb machining: A novel method based on digital image processing

α + β titanium alloys, namely Ti-6Al-7Nb, have found growing importance in the biomedical industry, in which CNC machining plays a decisive role in the production of high-quality components. Metal chip analysis is a consolidated method for machining optimization, particularly in difficult-to-cut materials that tend to form saw-tooth/segmented chips. A novel automatic method for quantification of classical geometric aspects of Ti-6Al-7Nb alloy saw-tooth chips was developed. Geometric features, including the minimum and maximum chip thickness, as well as the saw-tooth width, were evaluated. These aspects were obtained by association of optical microscopy and digital image processing. The advantage of this methodology is the fully automated analysis of the geometric aspects of the chip, which are generally obtained manually. The robustness of the measurements was ensured by the selection of shape descriptors tested on a range of cutting speeds. This work also delivers a set of valuable experimental results about Ti-6Al-7Nb machining.

Association between sub‐classified white matter hyperintensities (WMH) volume and and cognitive function in patients with Alzheimer`s disease

AbstractBackgroundWMH in MRI are common among the elderly. WMH are associated with increased cognitive impairment and risk of Alzheimer`s disease (AD). Although WMH contribute to AD pathology, their clinical implications are not fully understood. This study aimed to assess whether WMH predict AD and investigate the association between WMH (including subclassified WMH volume) and cognitive function in AD.MethodThis study enrolled 171 patients with AD. All participants underwent clinical evaluations including brain MRI study and neuropsychological tests using the CERAD‐K neuropsychological assessment battery. In addition, Charlson comorbidity index, the Korean version of the Geriatric Depression Scale short from were administered. WMH volume was calculated using automated quantification method with SPM and MATLAB image processing software. WMH were classified according to the distance from the ventricular surface. WMH located in juxtaventricular areas, within 3 mm from the ventricular surface, were classified as juxtaventricular with matter hyperintensities (JVWMH). WMH located within 3–13 mm or farther than 13 mm from the ventricular surface were classified as periventricular white matter hyperintensities (PVWMH) or deep white matter hyperintensities (DWMH), respectively. WMH volume data were right‐skewed. Consequently, WMH volume data were logarithmic transformed.ResultThe AD group had a higher mean WMH volume(20.7 ± 18.2 ml). Multivariate linear regression analysis showed that total WMH volume in AD was associated with poor performance in categorical verbal fluency test (p = 0.008) and word list memory test (p = 0.023). JVWMH volume in AD was associated with poor performance on categorical verbal fluency test (p = 0.013) and digit span test forward (p = 0.037). PVWMH volume in AD was associated with poor performance on categorical verbal fluency test (p = 0.011) and word list memory test (p = 0.021), whereas DWMH volume showed no association with cognitive dysfunction in AD.ConclusionTotal WMH, JVWMH and PVWMH were associated specifically with executive function, immediate memory, and working memory, independently of hippocampal atrophy in AD. WMH were associated with AD risk and cognitive dysfunction differentially according to the distance from the ventricular surface.

Fast and accurate amyloid brain PET quantification without MRI using deep neural networks

AbstractBackgroundWe propose a novel method for automatic quantification of amyloid PET using a deep learning‐based spatial normalization (SN) of PET images, which does not require corresponding MRI or CT image of the same patient. In this study, the accuracy of the proposed method was evaluated for three different amyloid PET radiotracers by comparison with MRI parcellation‐based PET quantification using FreeSurfer, which is more accurate than SN‐based method but requires significantly longer computation time.MethodDeep neural network model used for SN of amyloid PET images was trained using 994 multicenter amyloid PET images, as well as corresponding 3D MRIs of the patients with Alzheimer’s disease and mild cognitive impairments and cognitive normal subjects. The accuracy of SN and SUVR quantification accuracy relative to FreeSurfer‐based estimation was evaluated using other 148 PET images. Additional external validation was also performed using independent data set (30 18F‐Flutemetamol, 67 18F‐Florbetaben, and 39 18F‐Florbetapir). For the comparison, PET SN was also conducted using SPM12 program. Then, the quantification results using SN methods were compared with SUVR values obtained using on FreeSurfer in individual brain space. Reference region was cerebellar grey matter and AAL3 atlas was used to extract the regional SUVR values.ResultsThe quantification results using the proposed method showed stronger correlations with FreeSurfer estimates than SPM SN using MRI did. For example, the slope, y‐intercept and R2 value between SPM and FreeSurfer for global cortex were 0.869, 0.113 and 0.946, respectively. On the other hand, those for proposed method were 1.019, ‐0.016 and 0.986. The external validation study also demonstrated the better performance of proposed method without MR images than SPM with MRI. In most brain regions, it outperformed the SPM SN in terms of the linear regression parameters and intraclass correlation coefficients.ConclusionIn this study, we evaluated a novel deep learning‐based SN method, which allows quantitative analysis of amyloid brain PET images without structural MRI. The proposed quantification results showed strong correlation with MRI‐parcellation‐based amyloid PET quantification using FreeSurfer in all clinical amyloid radiotracers. Therefore, the proposed method will be useful for investigating Alzheimer’s disease and related brain disorders using amyloid PET scans.

Automated detection and quantification of contact behaviour in pigs using deep learning

Change in the frequency of contact between pigs within a group may be indicative of a change in the physiological or health status of one or more pigs within a group, or indicative of the occurrence of abnormal behaviour, e.g. tail-biting. Here, we developed a novel framework that detects and quantifies the frequency of interaction, i.e., a pig head to another pig rear, between pigs in groups. The method does not require individual pig tracking/identification and uses only inexpensive camera-based data capturing infrastructure. We modified the architecture of well-established deep learning models and further developed a lightweight processing stage that scans over pigs to score said interactions. This included the addition of a detection subnetwork to a selected layer of the base residual network. We first validated the automated system to score the interactions between individual pigs within a group, and determined an average accuracy of 92.65% ± 3.74%, under a variety of settings, e.g., management set-ups and data capturing. We then applied the method to a significant welfare challenge in pigs, that of the detection of tail-biting outbreaks in pigs and quantified the changes that happen in contact behaviour during such an outbreak. Our study shows that the system is able to accurately monitor pig interactions under challenging farming conditions, without the need for additional sensors or a pig tracking stage. The method has a number of potential applications to the field of precision livestock farming of pigs that may transform the industry. • An automated quantification method for pig social interactions was developed. • The automated method is based on convolutional deep neural networks. • Detection of pig interactions, such as head-to-rear is highly accurate (92.6%). • Robust method performance was achieved under different management settings. • The method detected changes in the interactions during a tail biting outbreak.

Evaluation of automatically generated tongue surfaces from three-dimensional/four-dimensional ultrasound recordings of sustained vowels

Three-dimensional/four-dimensional (3-D/4-D) ultrasound is capable of imaging almost the entire tongue surface during the production of speech sounds, including sustained vowels. Manual tongue surface segmentation is slow and laborious, but typically yields good results. Automatic tongue surface segmentation is much faster and objective but can result in errors. In this presentation, we quantify the errors in a set of tongue surfaces that were automatically segmented using the previously published 3D-SLURP algorithm implemented in the WASL toolbox for MATLAB. The ultrasound recordings were of 20 children and 40 adults producing the sustained vowel [o:] with and without a narrow straw semi-occlusion in the mouth. Twenty of the 40 adults produced phonation at threshold phonation. Automatic and manual error detection and quantification methods will be compared to evaluate the accuracy of the automated algorithm when applied to the sustained vowel [o:]. [Work supported in part by NSF.]

Human Bone Typing Using Quantitative Cone-Beam Computed Tomography

IntroductionBone typing is crucial to enable the choice of a suitable implant, the surgical technique, and the evaluation of the clinical outcome. Currently, bone typing is assessed subjectively by the surgeon. ObjectiveThe aim of this study is to establish an automatic quantification method to determine local bone types by the use of cone-beam computed tomography (CBCT) for an observer-independent approach. MethodsSix adult human cadaver skulls were used. The 4 generally used bone types in dental implantology and orthodontics were identified, and specific Hounsfield unit (HU) ranges (grey-scale values) were assigned to each bone type for identification by quantitative CBCT (qCBCT). The selected scanned planes were labelled by nonradiolucent markers for reidentification in the backup/cross-check evaluation methods. The selected planes were then physically removed as thick bone tissue sections for in vitro correlation measurements by qCBCT, quantitative micro–computed tomography (micro-CT), and quantitative histomorphometry. ResultsCorrelation analyses between the different bone tissue quantification methods to identify bone types based on numerical ranges of HU values revealed that the Pearson correlation coefficient of qCBCT with micro-CT and quantitative histomorphometry was R = 0.9 (P = .001) for all 4 bone types . ConclusionsWe found that qCBCT can reproducibly and objectively assess human bone types at implant sites.