Background:Segmentation of abdominal organs in computed tomography(CT)images within clinical oncological workflows is crucial for ensuring effective treatment planning and follow-up.However,manually generated segmenta...Background:Segmentation of abdominal organs in computed tomography(CT)images within clinical oncological workflows is crucial for ensuring effective treatment planning and follow-up.However,manually generated segmentations are time-consuming and labor-intensive in addition to being subject to inter-observer variability.Many deep learning and automated machine learning(AutoML)frameworks have emerged as a solution to this challenge and show promise in clinical workflows.Objective:This study aims to provide a comprehensive evaluation of existing AutoML frameworks(Auto3DSeg,nnU-Net)against a state-of-the-art non-AutoML framework,the Shifted Window U-Net Transformer(SwinUNETR).Methods:Each framework was trained on the same 122 training images,taken from the Abdominal Multi-Organ Segmentation(AMOS)Grand Challenge.Frameworks were compared using dice similarity coefficient(DSC),surface DSC(sDSC),and 95th percentile Hausdorff distances(HD95)on an additional 72 holdout-validation images.The perceived clinical viability of 30 auto-contoured test cases was assessed by three physicians in a blinded evaluation.Results:Comparisons show significantly better performance by AutoML methods:nnU-Net(average DSC:0.924,average sDSC:0.938,average HD95:4.26,median Likert:4.57),Auto3DSeg(average DSC:0.902,average sDSC:0.919,average HD95:8.76,median Likert:4.49),and SwinUNETR(average DSC:0.837,average sDSC:0.844,average HD95:13.93).AutoML frameworks were quantitatively preferred(13/13 organs at risks[OARs]P<0.05 in DSC and sDSC,12/13 OARs P<0.05 in HD95,comparing Auto3DSeg to SwinUNETR,and all OARs P<0.05 in all metrics comparing SwinUNETR to nnU-Net).Qualitatively,nnU-Net was preferred over Auto3DSeg(P=0.0027).Conclusion:The findings suggest that AutoML frameworks offer a significant advantage in the segmentation of abdominal organs,and underscores the potential of AutoML methods to enhance the efficiency of oncological workflows.展开更多
基金funding from the University of Alabama at Birmingham,the National Institutions of Health/National Cancer Institute Award(LRP0000018407)National Center for Advancing Translational Sciences(5KL2TR003097-05).
文摘Background:Segmentation of abdominal organs in computed tomography(CT)images within clinical oncological workflows is crucial for ensuring effective treatment planning and follow-up.However,manually generated segmentations are time-consuming and labor-intensive in addition to being subject to inter-observer variability.Many deep learning and automated machine learning(AutoML)frameworks have emerged as a solution to this challenge and show promise in clinical workflows.Objective:This study aims to provide a comprehensive evaluation of existing AutoML frameworks(Auto3DSeg,nnU-Net)against a state-of-the-art non-AutoML framework,the Shifted Window U-Net Transformer(SwinUNETR).Methods:Each framework was trained on the same 122 training images,taken from the Abdominal Multi-Organ Segmentation(AMOS)Grand Challenge.Frameworks were compared using dice similarity coefficient(DSC),surface DSC(sDSC),and 95th percentile Hausdorff distances(HD95)on an additional 72 holdout-validation images.The perceived clinical viability of 30 auto-contoured test cases was assessed by three physicians in a blinded evaluation.Results:Comparisons show significantly better performance by AutoML methods:nnU-Net(average DSC:0.924,average sDSC:0.938,average HD95:4.26,median Likert:4.57),Auto3DSeg(average DSC:0.902,average sDSC:0.919,average HD95:8.76,median Likert:4.49),and SwinUNETR(average DSC:0.837,average sDSC:0.844,average HD95:13.93).AutoML frameworks were quantitatively preferred(13/13 organs at risks[OARs]P<0.05 in DSC and sDSC,12/13 OARs P<0.05 in HD95,comparing Auto3DSeg to SwinUNETR,and all OARs P<0.05 in all metrics comparing SwinUNETR to nnU-Net).Qualitatively,nnU-Net was preferred over Auto3DSeg(P=0.0027).Conclusion:The findings suggest that AutoML frameworks offer a significant advantage in the segmentation of abdominal organs,and underscores the potential of AutoML methods to enhance the efficiency of oncological workflows.
