RCSegNeXt: Efficient multi-scale ConvNeXt for rectal cancer segmentation from sagittal MRI scans

Wang Bo, Ting Xue, Leyang Pan, Dingfu Huang, Yi Xiao, Li Fan, Zaiyi Liu, Shiyuan Liu, S Kevin Zhou

Keywords: rectal cancer, image segmentation, anisotropic MRI, multi-scale

Abstract:

Rectal cancer remains a critical global health challenge, significantly contributing to mor-
bidity and mortality worldwide. Magnetic resonance imaging (MRI) in a sagittal plane
offers distinct advantages for rectal cancer diagnosis by providing detailed visualization of
the rectum and its surrounding anatomy. However, automated segmentation of the rectum
and associated tumors remains difficult due to tumor heterogeneity and complex anatom-
ical structure, which necessitate multi-scale feature extraction. This study proposes RC-
SegNeXt, a novel non-uniform pure-convolutional rectal cancer segmentation architecture
that combines shallow anisotropic stages with deep isotropic stages. The anisotropic stages
leverage AniNeXt blocks, designed with customized convolutional kernels and pooling op-
erations to address the uneven spatial resolution inherent in MRI data. In the isotropic
stages, an IsoNeXt block with a Scale-Aware Integration Module (SAIM) enables efficient
multi-scale feature fusion by directing information flow through constrained pathways. This
design enhances computational efficiency while achieving superior segmentation accuracy.
Experiments on two in-house datasets demonstrate the proposed method’s state-of-the-art
performances. Code will be open upon acceptance.

Primary Subject Area: Segmentation

Secondary Subject Area: Integration of Imaging and Clinical Data

Paper Type: Both

Registration Requirement: Yes

Visa & Travel: Yes

Submission Number: 87