SegResMamba: An Efficient Architecture for 3D Medical Image Segmentation

Badhan Kumar Das, Ajay Singh, Saahil Islam, Gengyan Zhao, Andreas Maier

Keywords: Mamba, Vision Transformer, Medical Image Segmentation

TL;DR: We propose SegResMamba, a memory and computation efficient Mamba-based model for 3D medical image segmentation.

Abstract: The Transformer architecture has opened a new paradigm in the domain of deep learning with its ability to model long-range dependencies and capture global context and has outpaced the traditional Convolution Neural Networks (CNNs) in many aspects. However, applying Transformer models to 3D medical image datasets presents significant challenges due to their high training time, and memory requirements, which not only hinder scalability but also contribute to elevated CO$_2$ footprint. This has led to an exploration of alternative models that can maintain or even improve performance while being more efficient and environmentally sustainable. Recent advancements in Structured State Space Models (SSMs) effectively address some of the inherent limitations of Transformers, particularly their high memory and computational demands. Inspired by these advancements, we propose an efficient 3D segmentation model for medical imaging called SegResMamba, designed to reduce computation complexity, memory usage, training time, and environmental impact while maintaining high performance. Our model uses less than half the memory during training compared to other state-of-the-art (SOTA) architectures, achieving comparable performance with significantly reduced resource demands.

Primary Subject Area: Segmentation

Secondary Subject Area: Application: Radiology

Paper Type: Both

Registration Requirement: Yes

Visa & Travel: Yes

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Submission Number: 111