Is Long Range Sequential Modeling Necessary For Colorectal Tumor Segmentation?
Journal:
arXiv
Published Date:
Feb 10, 2025
Abstract
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.
