Advancing Abdominal Organ and PDAC Segmentation Accuracy with Task-Specific Interactive Models

Deep learning-based segmentation algorithms have the potential to expedite the cumbersome clinical task of creating detailed target delineations for disease diagnosis and prognosis. However, these algorithms have yet to be widely adopted in clinical practice, partly because the resulting model segmentations often fall short of the necessary accuracy and robustness that clinical practice demands. This research aims to make AI work in the real world, where domain shift is anticipated and inter-observer variability is inherent to medical practice. While current research aims to design models that can address these challenges, we propose an alternative approach that involves minimal user (clinician) interaction in the segmentation process. By combining the pattern recognition abilities of neural networks with the domain knowledge of clinicians, segmentation predictions can deliver the desired clinical result with little effort on the part of clinicians. To test this approach, we implemented, fine-tuned and compared three state-of-the-art (SOTA) interactive AI (IAI) methods for segmenting six different abdominal organs and pancreatic ductal adenocarcinoma (PDAC), an extremely challenging structure to segment, in CT images. We demonstrate that the fine-tuned RITM (Reviving Iterative Training with Mask Guidance for Interactive Segmentation) method can achieve higher segmentation accuracy than non-interactive SOTA models with as few as three clicks, potentially reducing the time required for treatment planning. Overall, IAI may be an effective method for bridging the gap between what deep learning-based segmentation algorithms have to offer and the high standard that is required for patient care.