SegMamba: The Future of 3D Medical Image Segmentation
Convolutional neural networks (CNNs) are great, but sometimes they struggle with big, detailed 3D medical images. That’s where SegMamba comes in. SegMamba is an innovative way of combining U-shape structure with Mamba to tackle the issue of large-size image features. It’s also great at modeling long-range dependencies within large volume data and is super quick.
The researchers at the Beijing Academy of Artificial Intelligence have conducted extensive experiments on the BraTS2023 dataset to confirm the efficiency of SegMamba in 3D medical image segmentation tasks. This approach excels in modeling whole-volume features while maintaining superior processing speed, even with big features at a resolution of 64 × 64 × 64.
To learn more about this groundbreaking method, check out the Paper and Github.
You don’t want to miss this new approach to 3D medical image segmentation!