Assessing Test-time Variability for Interactive 3D Medical Image Segmentation with Diverse Point Prompts
This work addresses variability in user expertise and ambiguous boundaries in medical imaging, offering a solution for more reliable interactive segmentation, though it is incremental as it builds on existing prompt engineering methods.
The paper tackles the problem of inconsistent prompt selection in interactive 3D medical image segmentation by assessing test-time variability with diverse point prompts, proposing an optimal strategy that improves segmentation accuracy, as demonstrated on the Medical Segmentation Decathlon dataset for colon tumor segmentation.
Interactive segmentation model leverages prompts from users to produce robust segmentation. This advancement is facilitated by prompt engineering, where interactive prompts serve as strong priors during test-time. However, this is an inherently subjective and hard-to-reproduce process. The variability in user expertise and inherently ambiguous boundaries in medical images can lead to inconsistent prompt selections, potentially affecting segmentation accuracy. This issue has not yet been extensively explored for medical imaging. In this paper, we assess the test-time variability for interactive medical image segmentation with diverse point prompts. For a given target region, the point is classified into three sub-regions: boundary, margin, and center. Our goal is to identify a straightforward and efficient approach for optimal prompt selection during test-time based on three considerations: (1) benefits of additional prompts, (2) effects of prompt placement, and (3) strategies for optimal prompt selection. We conduct extensive experiments on the public Medical Segmentation Decathlon dataset for challenging colon tumor segmentation task. We suggest an optimal strategy for prompt selection during test-time, supported by comprehensive results. The code is publicly available at https://github.com/MedICL-VU/variability