Organ Segmentation Research Articles

Deep Learning in Endoscopic Ultrasound: A Breakthrough in Detecting Distal Cholangiocarcinoma

Introduction: Cholangiocarcinoma (CCA) is a highly lethal malignancy originating in the bile ducts, often diagnosed late with poor prognosis. Differentiating benign from malignant biliary tumors remains challenging, necessitating advanced diagnostic techniques. Objective: This study aims to enhance the diagnostic accuracy of endoscopic ultrasound (EUS) for distal cholangiocarcinoma (dCCA) using advanced convolutional neural networks (CCNs) for the classification and segmentation of EUS images, specifically targeting dCCAs, the pancreas, and the bile duct. Materials and Methods: In this retrospective study, EUS images from patients diagnosed with dCCA via biopsy and an EUS-identified bile duct tumor were evaluated. A custom CNN was developed for classification, trained on 156 EUS images. To enhance the model’s robustness, image augmentation techniques were applied, generating a total of 1248 images. For tumor and organ segmentation, the DeepLabv3+ network with ResNet50 architecture was utilized, employing Tversky loss to manage unbalanced classes. Performance evaluation included metrics such as accuracy, sensitivity, specificity, and Intersection over Union (IoU). These methods were implemented in collaboration with the ADAPTED Research Group at the Technical University of Cluj-Napoca. Results: The classification model achieved a high accuracy of 97.82%, with precision and specificity both at 100% and sensitivity at 94.44%. The segmentation models for the pancreas and bile duct demonstrated global accuracies of 84% and 90%, respectively, with robust IoU scores indicating good overlap between predicted and actual contours. The application performed better than the UNet model, particularly in generalization and boundary delineation. Conclusions: This study demonstrates the significant potential of AI in EUS imaging for dCCA, presenting a robust tool that enhances diagnostic accuracy and efficiency. The developed MATLAB application serves as a valuable aid for medical professionals, facilitating informed decision-making and improving patient outcomes in the diagnosis of cholangiocarcinoma and related pathologies.

An Overview of Malaysia’s Retail Organic Foods Market and Its Consumption Behavior

Growing global awareness of health and environmental sustainability has significantly increased the demand for organic foods, a trend gaining traction in Malaysia. This research provides a comprehensive overview of consumer consumption patterns and purchase intentions for organic foods in Malaysia’s retail sector, identifying key factors influencing their choices. The study examines Malaysia’s broader food retail landscape, focusing on the growing organic segment and its evolving consumption patterns. It also explores the organic food market, considering domestic production and the availability of imported goods that shape consumer choices and market growth. The study offers practical insights into consumer decision-making within the retail food market by examining consumption behavior, such as purchase intentions. The review uncovers critical insights into the organic food market, including its size, rising demand and consumption trends, government and retailer initiatives, and the demographic profiles of consumers who purchase or intend to purchase organic foods. These findings contribute to existing research on organic food consumption in Malaysia and provide valuable guidance for policymakers, government agencies, and retailers aiming to enhance the adoption of organic foods in the Malaysian retail sector.

PP04 Presentation Time: 4:27 PM: Real-Time Catheter Tracking and Organ Segmentation in Prostate High-Dose-Rate Brachytherapy

PP04 Presentation Time: 4:27 PM: Real-Time Catheter Tracking and Organ Segmentation in Prostate High-Dose-Rate Brachytherapy

Impact of Artificial Intelligence-Based Autosegmentation of Organs at Risk in Low- and Middle-Income Countries

Radiation therapy (RT) processes require significant human resources and expertise, creating a barrier to rapid RT deployment in low- and middle-income countries (LMICs). Accurate segmentation of tumor targets and organs at risk (OARs) is crucial for optimal RT. This study assessed the impact of artificial intelligence (AI)-based autosegmentation of OARs in 2 LMICs. Ten patients, comprising 5 head and neck (HN) cancer patients and 5 prostate cancer patients, were randomly selected. Planning computed tomography images were subjected to autosegmentation using an Food and Drug Administration-approved AI software tool and manual segmentation by experienced radiation oncologists from 2 LMIC RT clinics. The control data, obtained from a large academic institution in the United States, consisted of contours obtained by an experienced radiation oncologist. The segmentation time, DICE similarity coefficient (DSC), Hausdorff distance, and mean surface distance were evaluated. AI significantly reduced segmentation time, averaging 2 minutes per patient, compared with 57 to 84 minutes for manual contouring in LMICs. Compared with the control data, the AI pelvic contours provided better agreement than did the LMIC manual contours (mean DSC of 0.834 vs 0.807 in LMIC1 and 0.844 vs 0.801 in LMIC2). For HN contours, AI provided better agreement for the majority of OAR contours than manual contours in LMIC1 (mean DSC: 0.823 vs 0.821) or LMIC2 (mean DSC: 0.792 vs 0.748). Neither the AI nor LMIC manual contours had good agreement with the control data (DSC < 0.600) for the optic nerves, chiasm, and cochlea. AI-based autosegmentation generates OAR contours of comparable quality to manual segmentation for both pelvic and HN cancer patients in LMICs, with substantial time savings.

Automated Organ Segmentation for Radiation Therapy: A Comparative Analysis of AI-Based Tools Versus Manual Contouring in Korean Cancer Patients.

Accurate delineation of tumors and organs at risk (OARs) is crucial for intensity-modulated radiation therapy. This study aimed to evaluate the performance of OncoStudio, an AI-based auto-segmentation tool developed for Korean patients, compared with Protégé AI, a globally developed tool that uses data from Korean cancer patients. A retrospective analysis of 1200 Korean cancer patients treated with radiotherapy was conducted. Auto-contours generated via OncoStudio and Protégé AI were compared with manual contours across the head and neck and thoracic, abdominal, and pelvic organs. Accuracy was assessed using the Dice similarity coefficient (DSC), mean surface distance (MSD), and 95% Hausdorff distance (HD). Feedback was obtained from 10 participants, including radiation oncologists, residents, and radiation therapists, via an online survey with a Turing test component. OncoStudio outperformed Protégé AI in 85% of the evaluated OARs (p < 0.001). For head and neck organs, OncoStudio achieved a similar DSC (0.70 vs. 0.70, p = 0.637) but significantly lower MSD and 95% HD values (p < 0.001). In thoracic organs, OncoStudio performed excellently in 90% of cases, with a significantly greater DSC (male: 0.87 vs. 0.82, p < 0.001; female: 0.95 vs. 0.87, p < 0.001). OncoStudio also demonstrated superior accuracy in abdominal (DSC 0.88 vs. 0.81, p < 0.001) and pelvic organs (male: DSC 0.95 vs. 0.85, p < 0.001; female: DSC 0.82 vs. 0.73, p < 0.001). Clinicians favored OncoStudio in 70% of cases, with 90% endorsing its clinical suitability for Korean patients. OncoStudio, which is tailored for Korean patients, demonstrated superior segmentation accuracy across multiple anatomical regions, suggesting its suitability for radiotherapy planning in this population.

Temporomandibular joint CBCT image segmentation via multi-view ensemble learning network.

Accurate segmentation of the temporomandibular joint (TMJ) from cone beam CT (CBCT) images holds significant clinical value for diagnosing temporomandibular joint osteoarthrosis (TMJOA) and related conditions. Convolutional neural network-based medical image segmentation methods have achieved state-of-the-art performance in various segmentation tasks. However, 3D medical images segmentation requires substantial global context and rich spatial semantic information, demanding much more GPU memory and computational resources. To address these challenges in 3D medical image segmentation, we propose a novel network- the MVEL-Net (Multi-view Ensemble Learning Network) for TMJ CBCT image segmentation. By resampling images along three dimensions, we generate multiple weak learners with different spatial semantic information. A subsequent strong learning network effectively integrates the outputs from these weak learners to achieve more accurate segmentation results. We evaluated our network model using a clinical dataset comprising 88 subjects with TMJ CBCT images. The average Dice similarity coefficient (DSC) was 0.9817 ± 0.0049, the average surface distance was 0.0540 ± 0.0179mm, and the 95% Hausdorff distance was 0.1743 ± 0.0550mm. Our proposed MVEL-Net demonstrates excellent segmentation performance on TMJ from CBCT images, while using fewer GPU memory resources compared to other 3D networks. The effectiveness of this method in capturing spatial context could be leveraged for tasks like organ segmentation from volumetric scans. This may facilitate wider adoption of AI-based solutions for automated analysis of 3D medical images.

PO0232: Automated Target and Organ Segmentation in Gyn Intracavitary and Interstitial Brachytherapy

PO0232: Automated Target and Organ Segmentation in Gyn Intracavitary and Interstitial Brachytherapy

Preclinical Positron Emission Tomography with Body Conforming Animal Molds for Cloud-Based Automated Image Analysis in Mice.

Positron Emission Tomography (PET) is a molecular imaging modality that can be used to investigate a multitude of pharmacological questions, such as biomarker modulation, receptor occupancy, and biodistribution of compounds of interest. In biodistribution studies, experimental subjects are often longitudinally imaged after receiving the test article. The images are then analyzed to derive the compound's distribution profile in various organs at different timepoints. This constitutes a crucial step in drug development to understand the distribution and potentially binding profile of an investigative compound. Standard/manual methods of PET imaging-based biodistribution analyses, however, are labor-intensive and time-consuming and are often associated with high inter-operator variability. Further, it is challenging to keep the animals' positions consistent across different timepoints. To address these shortcomings, a series of mouse Body Conforming Animal Molds (BCAMs) were used to enable rigid and consistent positioning of animals during PET/CT imaging acquisition. Further, a Software-as-a-Service (SaaS) platform consisting of a cloud-based Organ Probability Map (OPM) and an artificial intelligence-powered segmentation tool were employed to enable reliable and automated quantitation of in vivo PET imaging data. The workflow presented here includes (1) prepping mice for imaging with the BCAMs, including the proper implantation of subcutaneous tumors to be compatible with the molds, (2) acquiring PET/CT images with BCAMs using the G8 scanner, and 3) performing automated organ segmentation and biodistribution analysis using the cloud-based SaaS. [18F]FDG was used as an exemplar tracer here, but other biomarkers and/or radio-labeled compounds can be readily adapted into the workflow. This procedure can be executed accurately and effectively with minimal training, and the automated PET data analysis yielded satisfactory results consistent with the manual method.

Hyperparameter selection for dataset-constrained semantic segmentation: Practical machine learning optimization.

This paper provides a pedagogical example for systematic machine learning optimization in small dataset image segmentation, emphasizing hyperparameter selections. A simple process is presented for medical physicists to examine hyperparameter optimization. This is also applied to a case-study, demonstrating the benefit of the method. An unrestricted public Computed Tomography (CT) dataset, with binary organ segmentation, was used to develop a multiclass segmentation model. To start the optimization process, a preliminary manual search of hyperparameters was conducted and from there a grid search identified the most influential result metrics. A total of 658 different models were trained in 2100h, using 13160 effective patients. The quantity of results was analyzed using random forest regression, identifying relative hyperparameter impact. Metric implied segmentation quality (accuracy 96.8%, precision 95.1%) and visual inspection were found to be mismatched. In this work batch normalization was most important, but performance varied with hyperparameters and metrics selected. Targeted grid-search optimization and random forest analysis of relative hyperparameter importance, was an easily implementable sensitivity analysis approach. The proposed optimization method gives a systematic and quantitative approach to something intuitively understood, that hyperparameters change model performance. Even just grid search optimization with random forest analysis presented here can be informative within hardware and data quality/availability limitations, adding confidence to model validity and minimize decision-making risks. By providing a guided methodology, this work helps medical physicists to improve their model optimization, irrespective of specific challenges posed by datasets and model design.

A comparative study on automatic treatment planning for online adaptive proton therapy of esophageal cancer: which combination of deformable registration and deep learning planning tools performs the best?

Objective.To demonstrate the feasibility of integrating fully-automated online adaptive proton therapy strategies (OAPT) within a commercially available treatment planning system and underscore what limits their clinical implementation. These strategies leverage existing deformable image registration (DIR) algorithms and state-of-the-art deep learning (DL) networks for organ segmentation and proton dose prediction.Approach.Four OAPT strategies featuring automatic segmentation and robust optimization were evaluated on a cohort of 17 patients, each undergoing a repeat CT scan. (1) DEF-INIT combines deformably registered contours with template-based optimization. (2) DL-INIT, (3) DL-DEF, and (4) DL-DL employ a nnU-Net DL network for organ segmentation and a controlling ROIs-guided DIR algorithm for internal clinical target volume (iCTV) segmentation. DL-INIT uses this segmentation alongside template-based optimization, DL-DEF integrates it with a dose-mimicking (DM) step using a reference deformed dose, and DL-DL merges it with DM on a reference DL-predicted dose. All strategies were evaluated on manual contours and contours used for optimization and compared with manually adapted plans. Key dose volume metrics like iCTV D98% are reported.Main results.iCTV D98% was comparable in manually adapted plans and for all strategies in nominal cases but dropped to 20 Gy in worst-case scenarios for a few patients per strategy, highlighting the need to correct segmentation errors in the target volume. Evaluations on optimization contours showed minimal relative error, with some outliers, particularly in template-based strategies (DEF-INIT and DL-INIT). DL-DEF achieves a good trade-off between speed and dosimetric quality, showing a passing rate (iCTV D98% > 94%) of 90% when evaluated against 2, 4 and 5 mm setup error and of 88% when evaluated against 7 mm setup error. While template-based methods are more rigid, DL-DEF and DL-DL have potential for further enhancements with proper DM algorithm tuning.Significance.Among investigated strategies, DL-DEF and DL-DL demonstrated promising within 10 min OAPT implementation results and significant potential for improvements.

A cascaded FAS-UNet+ framework with iterative optimization strategy for segmentation of organs at risk.

Segmentation of organs at risks (OARs) in the thorax plays a critical role in radiation therapy for lung and esophageal cancer. Although automatic segmentation of OARs has been extensively studied, it remains challenging due to the varying sizes and shapes of organs, as well as the low contrast between the target and background. This paper proposes a cascaded FAS-UNet+ framework, which integrates convolutional neural networks and nonlinear multi-grid theory to solve a modified Mumford-shah model for segmenting OARs. This framework is equipped with an enhanced iteration block, a coarse-to-fine multiscale architecture, an iterative optimization strategy, and a model ensemble technique. The enhanced iteration block aims to extract multiscale features, while the cascade module is used to refine coarse segmentation predictions. The iterative optimization strategy improves the network parameters to avoid unfavorable local minima. An efficient data augmentation method is also developed to train the network, which significantly improves its performance. During the prediction stage, a weighted ensemble technique combines predictions from multiple models to refine the final segmentation. The proposed cascaded FAS-UNet+ framework was evaluated on the SegTHOR dataset, and the results demonstrate significant improvements in Dice score and Hausdorff Distance (HD). The Dice scores were 95.22%, 95.68%, and HD values were 0.1024, and 0.1194 for the segmentations of the aorta and heart in the official unlabeled dataset, respectively. Our code and trained models are available at https://github.com/zhuhui100/C-FASUNet-plus .

A Two-Phase Methodology leveraging X-DenseNet for Multi-Organ Segmentation in Abdominal CT Scans

Introduction: Multi organ segmentation (MOS) is crucial for various medical applications such as disease diagnosis, treatment planning, and surgery. Manual segmentation of CT organs is inefficient, laborious and time-consuming, necessitating the development of automated techniques. To address these challenges, automatic techniques based on deep learning models are explored in this field. These techniques require improvements to deal with patient-to-patient variability in organ size, location, and shape. Despite the transformative potential of deep learning techniques, research studies often inadequately address low contrast and overlapping structures within CT scans. This paper proposes a novel two phase methodology to integrate deep learning models and image enhancement strategies. Methods: Our framework contributes in two stages named (a) Optimized Contrast Limited Adaptive Histogram Equalization-Weighted Grey Wolf Optimization (optiCLAHE-Weighted GWO), where the organs contrast is enhanced by employing weighted GWO for CLAHE clip limit selection (b) X-DenseNet architecture, where segmented regions of interest are produced with X-DenseNet model from the enhanced CT. Results: For validation of proposed multi organ segmentation (PMOS) technique, experimentation and comparative analysis with existing models has been conducted on FLARE 22 challenge dataset. The Dice Score (DSC) of liver, aorta and spleen for proposed Multi organ segmentation (PMOS) approach is 90.4%, 99.38% and 98.33% respectively. The mean of Precision (Pre), Accuracy (Acc), F-score and DSC of the proposed approach are 95.05%, 95.55%, 95.29% and 96.06% respectively.

Applications of artificial intelligence in interventional oncology: An up-to-date review of the literature.

Interventional oncology provides image-guided therapies, including transarterial tumor embolization and percutaneous tumor ablation, for malignant tumors in a minimally invasive manner. As in other medical fields, the application of artificial intelligence (AI) in interventional oncology has garnered significant attention. This narrative review describes the current state of AI applications in interventional oncology based on recent literature. A literature search revealed a rapid increase in the number of studies relevant to this topic recently. Investigators have attempted to use AI for various tasks, including automatic segmentation of organs, tumors, and treatment areas; treatment simulation; improvement of intraprocedural image quality; prediction of treatment outcomes; and detection of post-treatment recurrence. Among these, the AI-based prediction of treatment outcomes has been the most studied. Various deep and conventional machine learning algorithms have been proposed for these tasks. Radiomics has often been incorporated into prediction and detection models. Current literature suggests that AI is potentially useful in various aspects of interventional oncology, from treatment planning to post-treatment follow-up. However, most AI-based methods discussed in this review are still at the research stage, and few have been implemented in clinical practice. To achieve widespread adoption of AI technologies in interventional oncology procedures, further research on their reliability and clinical utility is necessary. Nevertheless, considering the rapid research progress in this field, various AI technologies will be integrated into interventional oncology practices in the near future.

Development and evaluation of machine-learning methods in whole-body magnetic resonance imaging with diffusion weighted imaging for staging of patients with cancer: the MALIBO diagnostic test accuracy study

Background Whole-body magnetic resonance imaging is accurate, efficient and cost-effective for cancer staging. Machine learning may support radiologists reading whole-body magnetic resonance imaging. Objectives To develop a machine-learning algorithm to detect normal organs and cancer lesions. To compare diagnostic accuracy, time and agreement of radiology reads to detect metastases using whole-body magnetic resonance imaging with concurrent machine learning (whole-body magnetic resonance imaging + machine learning) against standard whole-body magnetic resonance imaging (whole-body magnetic resonance imaging + standard deviation). Design and participants Retrospective analysis of (1) prospective single-centre study in healthy volunteers &gt; 18 years (n = 51) and (2) prospective multicentre STREAMLINE study patient data (n = 438). Tests Index: whole-body magnetic resonance imaging + machine learning. Comparator: whole-body magnetic resonance imaging + standard deviation. Reference standard Previously established expert panel consensus reference at 12 months from diagnosis. Outcome measures Primary: difference in per-patient specificity between whole-body magnetic resonance imaging + machine learning and whole-body magnetic resonance imaging + standard deviation. Secondary: per-patient sensitivity, per-lesion sensitivity and specificity, read time and agreement. Methods Phase 1: classification forests, convolutional neural networks, and a multi-atlas approaches for organ segmentation. Phase 2/3: whole-body magnetic resonance imaging scans were allocated to Phase 2 (training = 226, validation = 45) and Phase 3 (testing = 193). Disease sites were manually labelled. The final algorithm was applied to 193 Phase 3 cases, generating probability heatmaps. Twenty-five radiologists (18 experienced, 7 inexperienced in whole-body magnetic resonance imaging) were randomly allocated whole-body magnetic resonance imaging + machine learning or whole-body magnetic resonance imaging + standard deviation over two or three rounds in a National Health Service setting. Read time was independently recorded. Results Phases 1 and 2: convolutional neural network had best Dice similarity coefficient, recall and precision measurements for healthy organ segmentation. Final algorithm used a ‘two-stage’ initial organ identification followed by lesion detection. Phase 3: evaluable scans (188/193, of which 50 had metastases from 117 colon, 71 lung cancer cases) were read between November 2019 and March 2020. For experienced readers, per-patient specificity for detection of metastases was 86.2% (whole-body magnetic resonance imaging + machine learning) and 87.7% (whole-body magnetic resonance imaging + standard deviation), (difference −1.5%, 95% confidence interval −6.4% to 3.5%; p = 0.387); per-patient sensitivity was 66.0% (whole-body magnetic resonance imaging + machine learning) and 70.0% (whole-body magnetic resonance imaging + standard deviation) (difference −4.0%, 95% confidence interval −13.5% to 5.5%; p = 0.344). For inexperienced readers (53 reads, 15 with metastases), per-patient specificity was 76.3% in both groups with sensitivities of 73.3% (whole-body magnetic resonance imaging + machine learning) and 60.0% (whole-body magnetic resonance imaging + standard deviation). Per-site specificity remained high within all sites; above 95% (experienced) or 90% (inexperienced). Per-site sensitivity was highly variable due to low number of lesions in each site. Reading time lowered under machine learning by 6.2% (95% confidence interval −22.8% to 10.0%). Read time was primarily influenced by read round with round 2 read times reduced by 32% (95% confidence interval 20.8% to 42.8%) overall with subsequent regression analysis showing a significant effect (p = 0.0281) by using machine learning in round 2 estimated as 286 seconds (or 11%) quicker. Interobserver variance for experienced readers suggests moderate agreement, Cohen’s κ = 0.64, 95% confidence interval 0.47 to 0.81 (whole-body magnetic resonance imaging + machine learning) and Cohen’s κ = 0.66, 95% confidence interval 0.47 to 0.81 (whole-body magnetic resonance imaging + standard deviation). Limitations Patient whole-body magnetic resonance imaging data were heterogeneous with relatively few metastatic lesions in a wide variety of locations, making training and testing difficult and hampering evaluation of sensitivity. Conclusions There was no difference in diagnostic accuracy for whole-body magnetic resonance imaging radiology reads with or without machine-learning support, although radiology read time may be slightly shortened using whole-body magnetic resonance imaging + machine learning. Future work Failure-case analysis to improve model training, automate lesion segmentation and transfer of machine-learning techniques to other tumour types and imaging modalities. Study registration This study is registered as ISRCTN23068310. Funding This award was funded by the National Institute for Health and Care Research (NIHR) Efficacy and Mechanism Evaluation (EME) programme (NIHR award ref: 13/122/01) and is published in full in Efficacy and Mechanism Evaluation; Vol. 11, No. 15. See the NIHR Funding and Awards website for further award information.

Navigating the green beauty boom: Unveiling the influence of social media on consumer behavior in the organic cosmetics industry

It has been evident in the recent years that the natural and organic segment particularly in the beauty industry is steadily growing with people’s awareness of the health effects of using natural products. Today, social networks are the main driving force affecting consumer attitude and their behavior concerning this sector. This paper will seek to evaluate the utility of social media and the purchase pattern in the organic cosmetics sector. The ‘literature review outlines several that have led to what is commonly referred to as the ‘green beauty boom’ The theoretical framework entails theories from the marketing channel that regards consumer behavior within the social media and eWOM contexts. The research designed consisted of a written self- administered survey and face- to face interviews with fifteen consumers of organic beauty products within the age range of 18-35 years whereby data was gathered and analyzed qualitatively by the use of thematic analysis. Stylized facts also establish how the content on social media platforms and reviews influence distinct stages in the consumer decision process of organic cosmetics. Of all the mentioned evaluative resources, those commonly used by consumers include resources accessed from social media sites. Moreover, as to the communities of people, who are interested in organic lifestyles or natural products influencing the degree of positive attitudes in the usage of natural ingredients and environmental sources. Consequently, the author discusses the subsequent marketing implications for the brands based on the paper and provides ideas for the subsequent brand development, the opportunities, and the possible ethical issues that should be considered by brands in the context of this area. From this study further research can be made to contribute to the mass pool of data that helps in understanding new media that constitutes consumer culture today.

Impact of breathing signal-guided dose modulation on step-and-shoot 4DCT image reconstruction.

Breathing signal-guided 4DCT sequence scanning such as the intelligent 4DCT (i4DCT) approach reduces imaging artifacts compared to conventional 4DCT. By design, i4DCT captures entire breathing cycles during beam-on periods, leading to redundant projection data and increased radiation exposure to patients exhibiting prolonged exhalation phases. A recently proposed breathing-guided dose modulation (DM) algorithm promises to lower the imaging dose by temporarily reducing the CT tube current, but the impact on image reconstruction and the resulting images have not beeninvestigated. We evaluate the impact of breathing signal-guided DM on 4DCT image reconstruction and correspondingimages. This study is designed as a comparative and retrospective analysis based on 104 4DCT datasets. Each dataset underwent retrospective reconstruction twice: (a) utilizing the acquired clinical projection data for reconstruction, which yields reference image data, and (b) excluding projections acquired during potential DM phases from image reconstruction, resulting in DM-affected image data. Resulting images underwent automatic organ segmentation (lung/liver). (Dis)Similarity of reference and DM-affected images were quantified by the Dice coefficient of the entire organ masks and the organ overlaps within the DM-affected slices. Further, for lung cases, (a) and (b) were deformably registered and median magnitudes of the obtained displacement field were computed. Eventually, for 17 lung cases, gross tumor volumes (GTV) were recontoured on both (a) and (b). Target volume similarity was quantified by the Hausdorffdistance. DM resulted in a median imaging dose reduction of 15.4% (interquartile range [IQR]: 11.3%-19.9%) for the present patient cohort. Dice coefficients for lung ( ) and liver ( ) patients were consistently high for both the entire organs and the DM-affected slices (IQR lung: [entire lung/DM-affected slices only] to ; IQR liver: to ), demonstrating that DM did not cause organ distortions or alterations. Median displacements for DM-affected to reference image registration varied; however, only two out of 73 cases exhibited a median displacement larger than one isotropic 1 voxel size. The impact on GTV definition for the end-exhalation phase was also minor (median Hausdorff distance: 0.38mm, IQR: 0.15-0.46mm). This study demonstrates that breathing signal-guided DM has a minimal impact on image reconstruction and image appearance while improving patient safety by reducing doseexposure.

Fuzzy Attention-Based Border Rendering Orthogonal Network for Lung Organ Segmentation

Fuzzy Attention-Based Border Rendering Orthogonal Network for Lung Organ Segmentation

Systemic Metabolic and Volumetric Assessment via Whole-Body [18F]FDG-PET/CT: Pancreas Size Predicts Cachexia in Head and Neck Squamous Cell Carcinoma.

Background/Objectives: Cancer-associated cachexia in head and neck squamous cell carcinoma (HNSCC) is challenging to diagnose due to its complex pathophysiology. This study aimed to identify metabolic biomarkers linked to cachexia and survival in HNSCC patients using [18F]FDG-PET/CT imaging and machine learning (ML) techniques. Methods: We retrospectively analyzed 253 HNSCC patients from Vienna General Hospital and the MD Anderson Cancer Center. Automated organ segmentation was employed to quantify metabolic and volumetric data from [18F]FDG-PET/CT scans across 29 tissues and organs. Patients were categorized into low weight loss (LoWL; grades 0-2) and high weight loss (HiWL; grades 3-4) groups, according to the weight loss grading system (WLGS). Machine learning models, combined with Cox regression, were used to identify survival predictors. Shapley additive explanation (SHAP) analysis was conducted to determine the significance of individual features. Results: The HiWL group exhibited increased glucose metabolism in skeletal muscle and adipose tissue (p = 0.01), while the LoWL group showed higher lung metabolism. The one-year survival rate was 84.1% in the LoWL group compared to 69.2% in the HiWL group (p < 0.01). Pancreatic volume emerged as a key biomarker associated with cachexia, with the ML model achieving an AUC of 0.79 (95% CI: 0.77-0.80) and an accuracy of 0.82 (95% CI: 0.81-0.83). Multivariate Cox regression confirmed pancreatic volume as an independent prognostic factor (HR: 0.66, 95% CI: 0.46-0.95; p < 0.05). Conclusions: The integration of metabolic and volumetric data provided a strong predictive model, highlighting pancreatic volume as a key imaging biomarker in the metabolic assessment of cachexia in HNSCC. This finding enhances our understanding and may improve prognostic evaluations and therapeutic strategies.

OAR-UNet: Enhancing Long-Distance Dependencies for Head and Neck OAR Segmentation

Accurate segmentation of organs at risk (OARs) is a crucial step in the precise planning of radiotherapy for head and neck tumors. However, manual segmentation methods using CT images, which are still predominantly applied in clinical settings, are inefficient and expensive. Additionally, existing segmentation methods struggle with small organs and have difficulty managing the complex interdependencies between organs. To address these issues, this study proposed an OAR-UNet segmentation method based on a U-shaped architecture with two key designs. To tackle the challenge of segmenting small organs, a Local Feature Perception Module (LFPM) is developed to enhance the sensitivity of the method to subtle structures. Furthermore, a Cross-shaped Transformer Block (CSTB) with a cross-shaped attention mechanism is introduced to improve the ability of the model to capture and process long-distance dependency information. To accelerate the convergence of the Transformer, we designed a Local Encoding Module (LEM) based on depthwise separable convolutions. In our experimental evaluation, we utilized two publicly available datasets, SegRap2023 and PDDCA, achieving Dice coefficients of 78.22% and 89.42%, respectively. These results demonstrate that our method outperforms both previous classic methods and state-of-the-art (SOTA) methods.

Cross‐Institutional Prediction System for Estimating Patient‐Specific Organ Dose from Chest CT Scans without Segmenting Internal Organs

This study aims to establish and validate a cross‐institutional prediction system for estimating patient‐specific organ doses from chest CT scans. By collaborating across multiple institutions, the study seeks to develop models that allow rapid online estimation of organ doses, eliminating the need for complex organ segmentation. Researchers delineate skin outlines from chest CT images obtained from two different institutions. Radiomics features are extracted from the chest CT data and skin contours. Organ doses are computed using Monte Carlo simulations as reference organ doses. Single‐ and cross‐institutional support vector regression (SVR) models are trained with radiomics features to predict organ doses from chest CT scans. Model performance is assessed using metrics like maean absolute percentage error (MAPE) and R‐squared (R2). For chest organs (lungs, heart, spinal cord, trachea, and esophagus), single‐institutional SVR models achieve MAPE values ranging from 4.72% to 15.31% and R2 values from 0.73 to 0.93. Cross‐institutional models have MAPE values in the range of 2.16–7.49% and R2 values from 0.84 to 0.99. SVR models trained with radiomics features from skin outlines can reliably estimate organ doses from chest CT scans. The proposed method is applicable across different institutions, and cross‐institutional models enhance predictive accuracy, generality, and robustness.