This AI Paper from China Introduces SegMamba: A Novel 3D Medical Image Segmentation Mamba Model Designed to Effectively Capture Long-Range Dependencies within Whole Volume Features at Every Scale

Enhancing the receptive area of fashions is essential for efficient 3D medical picture segmentation. Conventional convolutional neural networks (CNNs) usually wrestle to seize world info from high-resolution 3D medical pictures. One proposed resolution is the utilization of depth-wise convolution with bigger kernel sizes to seize a wider vary of options. Nevertheless, CNN-based approaches want assist in capturing relationships throughout distant pixels.

Lately, there was an intensive exploration of transformer architectures, leveraging self-attention mechanisms to extract world info for 3D medical picture segmentation like TransBTS, which mixes 3D-CNN with transformers to seize each native spatial options and world dependencies in high-level options; UNETR, which adopts the Imaginative and prescient Transformer (ViT) as its encoder to study contextual info. Nevertheless, transformer-based strategies usually face computational challenges as a result of excessive decision of 3D medical pictures, resulting in lowered pace efficiency.

To handle the problems of lengthy sequence modeling, researchers have beforehand launched Mamba, a state area mannequin (SSM), to mannequin long-range dependencies effectively via a variety mechanism and a hardware-aware algorithm. Numerous research have utilized Mamba in laptop imaginative and prescient (CV) duties. As an example, U-Mamba integrates the Mamba layer to enhance medical picture segmentation. 

On the identical time, Imaginative and prescient Mamba proposes the Vim block, incorporating bidirectional SSM for world visible context modeling and place embeddings for location-aware understanding. VMamba additionally introduces a CSM module to bridge the hole between 1-D array scanning and 2-D plain traversing. Nevertheless, conventional transformer blocks face challenges in dealing with large-size options, necessitating the modeling of correlations inside high-dimensional options for enhanced visible understanding.

Motivated by this, researchers on the Beijing Academy of Synthetic Intelligence launched SegMamba, a novel structure combining the U-shape construction with Mamba to mannequin whole-volume world options at varied scales. They make the most of Mamba particularly for 3D medical picture segmentation. SegMamba demonstrates outstanding capabilities in modeling long-range dependencies inside volumetric information whereas sustaining excellent inference effectivity in comparison with conventional CNN-based and transformer-based strategies.

The researchers performed Intensive experiments on the BraTS2023 dataset to affirm SegMamba’s effectiveness and effectivity in 3D medical picture segmentation duties. In contrast to Transformer-based strategies, SegMamba leverages the ideas of state area modeling to excel in modeling whole-volume options whereas sustaining superior processing pace. Even with quantity options at a decision of 64 × 64 × 64 (equal to a sequential size of about 260k), SegMamba showcases outstanding effectivity.

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