BackgroundSegmentation 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.ObjectiveThis 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).MethodsEach 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.ResultsComparisons 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).ConclusionThe 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.