Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Image Segmentation

07/29/2022
by   Shuchao Pang, et al.
0

Automatic tumor or lesion segmentation is a crucial step in medical image analysis for computer-aided diagnosis. Although the existing methods based on Convolutional Neural Networks (CNNs) have achieved the state-of-the-art performance, many challenges still remain in medical tumor segmentation. This is because, although the human visual system can detect symmetries in 2D images effectively, regular CNNs can only exploit translation invariance, overlooking further inherent symmetries existing in medical images such as rotations and reflections. To solve this problem, we propose a novel group equivariant segmentation framework by encoding those inherent symmetries for learning more precise representations. First, kernel-based equivariant operations are devised on each orientation, which allows it to effectively address the gaps of learning symmetries in existing approaches. Then, to keep segmentation networks globally equivariant, we design distinctive group layers with layer-wise symmetry constraints. Finally, based on our novel framework, extensive experiments conducted on real-world clinical data demonstrate that a Group Equivariant Res-UNet (named GER-UNet) outperforms its regular CNN-based counterpart and the state-of-the-art segmentation methods in the tasks of hepatic tumor segmentation, COVID-19 lung infection segmentation and retinal vessel detection. More importantly, the newly built GER-UNet also shows potential in reducing the sample complexity and the redundancy of filters, upgrading current segmentation CNNs and delineating organs on other medical imaging modalities.

READ FULL TEXT

page 6

page 8

page 12

research
05/08/2020

Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Images for Segmentation

Automatic tumor segmentation is a crucial step in medical image analysis...
research
04/05/2023

FMG-Net and W-Net: Multigrid Inspired Deep Learning Architectures For Medical Imaging Segmentation

Accurate medical imaging segmentation is critical for precise and effect...
research
01/11/2017

CNN-based Segmentation of Medical Imaging Data

Convolutional neural networks have been applied to a wide variety of com...
research
04/17/2023

When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation

Learning to segmentation without large-scale samples is an inherent capa...
research
03/28/2023

Medical Image Analysis using Deep Relational Learning

In the past ten years, with the help of deep learning, especially the ra...
research
07/20/2020

Distractor-Aware Neuron Intrinsic Learning for Generic 2D Medical Image Classifications

Medical image analysis benefits Computer Aided Diagnosis (CADx). A funda...
research
08/23/2023

Tumor-Centered Patching for Enhanced Medical Image Segmentation

The realm of medical image diagnosis has advanced significantly with the...

Please sign up or login with your details

Forgot password? Click here to reset