Performance of Commercial Deep Learning-Based Auto-Segmentation Software for Prostate Cancer Radiation Therapy Planning: A Systematic Review DOI Creative Commons
Curtise K. C. Ng

Information, Год журнала: 2025, Номер 16(3), С. 215 - 215

Опубликована: Март 11, 2025

As yet, there is no systematic review focusing on benefits and issues of commercial deep learning-based auto-segmentation (DLAS) software for prostate cancer (PCa) radiation therapy (RT) planning despite that NRG Oncology has underscored such necessity. This article’s purpose to systematically DLAS product performances PCa RT their associated evaluation methodology. A literature search was performed with the use electronic databases 7 November 2024. Thirty-two articles were included as per selection criteria. They evaluated 12 products (Carina Medical LLC INTContour (Lexington, KY, USA), Elekta AB ADMIRE (Stockholm, Sweden), Limbus AI Inc. Contour (Regina, SK, Canada), Manteia Technologies Co. AccuContour (Jian Sheng, China), MIM Software ProtégéAI (Cleveland, OH, Mirada Ltd. DLCExpert (Oxford, UK), MVision.ai Contour+ (Helsinki, Finland), Radformation AutoContour (New York, NY, RaySearch Laboratories RayStation Siemens Healthineers AG AI-Rad Companion Organs RT, syngo.via Image Suite DirectORGANS (Erlangen, Germany), Therapanacea Annotate (Paris, France), Varian Systems, Ethos (Palo Alto, CA, USA)). Their results illustrate can delineate organs at risk (abdominopelvic cavity, anal canal, bladder, body, cauda equina, left (L) right (R) femurs, L R pelvis, proximal sacrum) four clinical target volumes (prostate, lymph nodes, bed, seminal vesicle bed) clinically acceptable outcomes, resulting in delineation time reduction, 5.7–81.1%. Although recommended each centre perform its own prior implementation, seems more important due methodological respective single studies, e.g., small dataset used, etc.

Язык: Английский

Automated Audit and Self-Correction Algorithm for Seg-Hallucination Using MeshCNN-Based On-Demand Generative AI DOI Creative Commons
Sihwan Kim, Chang-Min Park,

Gi Joon Jeon

и другие.

Bioengineering, Год журнала: 2025, Номер 12(1), С. 81 - 81

Опубликована: Янв. 16, 2025

Recent advancements in deep learning have significantly improved medical image segmentation. However, the generalization performance and potential risks of data-driven models remain insufficiently validated. Specifically, unrealistic segmentation predictions deviating from actual anatomical structures, known as a Seg-Hallucination, often occur learning-based models. The Seg-Hallucinations can result erroneous quantitative analyses distort critical imaging biomarker information, yet effective audits or corrections to address these issues are rare. Therefore, we propose an automated Seg-Hallucination surveillance correction (ASHSC) algorithm utilizing only 3D organ mask information derived CT images without reliance on ground truth. Two publicly available datasets were used developing ASHSC algorithm: 280 scans TotalSegmentator dataset for training 274 Cancer Imaging Archive (TCIA) evaluation. utilizes two-stage on-demand strategy with mesh-based convolutional neural networks generative artificial intelligence. quality level (SQ-level)-based stage was evaluated using area under receiver operating curve, sensitivity, specificity, positive predictive value. assessed similarity metrics: volumetric Dice score, volume error percentage, average surface distance, Hausdorff distance. Average resulted AUROC 0.94 ± 0.01, sensitivity 0.82 0.03, specificity 0.90 PPV 0.92 0.01 test dataset. After refinement stage, all four metrics compared single use AI-segmentation model. This study not enhances efficiency reliability handling but also eliminates offers intuitive guidance uncertainty regions, while maintaining manageable computational complexity. SQ-level-based adaptively minimizes uncertainties inherent deep-learning-based masks advances auditing methodologies.

Язык: Английский

Процитировано

0
Performance of Commercial Deep Learning-Based Auto-Segmentation Software for Prostate Cancer Radiation Therapy Planning: A Systematic Review DOI Creative Commons
Curtise K. C. Ng

Information, Год журнала: 2025, Номер 16(3), С. 215 - 215

Опубликована: Март 11, 2025

As yet, there is no systematic review focusing on benefits and issues of commercial deep learning-based auto-segmentation (DLAS) software for prostate cancer (PCa) radiation therapy (RT) planning despite that NRG Oncology has underscored such necessity. This article’s purpose to systematically DLAS product performances PCa RT their associated evaluation methodology. A literature search was performed with the use electronic databases 7 November 2024. Thirty-two articles were included as per selection criteria. They evaluated 12 products (Carina Medical LLC INTContour (Lexington, KY, USA), Elekta AB ADMIRE (Stockholm, Sweden), Limbus AI Inc. Contour (Regina, SK, Canada), Manteia Technologies Co. AccuContour (Jian Sheng, China), MIM Software ProtégéAI (Cleveland, OH, Mirada Ltd. DLCExpert (Oxford, UK), MVision.ai Contour+ (Helsinki, Finland), Radformation AutoContour (New York, NY, RaySearch Laboratories RayStation Siemens Healthineers AG AI-Rad Companion Organs RT, syngo.via Image Suite DirectORGANS (Erlangen, Germany), Therapanacea Annotate (Paris, France), Varian Systems, Ethos (Palo Alto, CA, USA)). Their results illustrate can delineate organs at risk (abdominopelvic cavity, anal canal, bladder, body, cauda equina, left (L) right (R) femurs, L R pelvis, proximal sacrum) four clinical target volumes (prostate, lymph nodes, bed, seminal vesicle bed) clinically acceptable outcomes, resulting in delineation time reduction, 5.7–81.1%. Although recommended each centre perform its own prior implementation, seems more important due methodological respective single studies, e.g., small dataset used, etc.

Язык: Английский

Процитировано

0