Is Long Range Sequential Modeling Necessary For Colorectal Tumor Segmentation?
This work is significant for researchers and clinicians working on 3D medical image segmentation, particularly for colorectal cancer tumor segmentation, as it challenges the necessity of long-range sequential modeling for this task.
The authors tackled the problem of colorectal tumor segmentation in 3D medical imaging and found that robust local token interactions can outperform long-range modeling techniques, achieving high accuracy in cases with small and anatomically complex regions of interest. Their proposed MambaOutUNet achieved this without the need for long-range sequential modeling.
Segmentation of colorectal cancer (CRC) tumors in 3D medical imaging is both complex and clinically critical, providing vital support for effective radiation therapy planning and survival outcome assessment. Recently, 3D volumetric segmentation architectures incorporating long-range sequence modeling mechanisms, such as Transformers and Mamba, have gained attention for their capacity to achieve high accuracy in 3D medical image segmentation. In this work, we evaluate the effectiveness of these global token modeling techniques by pitting them against our proposed MambaOutUNet within the context of our newly introduced colorectal tumor segmentation dataset (CTS-204). Our findings suggest that robust local token interactions can outperform long-range modeling techniques in cases where the region of interest is small and anatomically complex, proposing a potential shift in 3D tumor segmentation research.