CATS v2: Hybrid encoders for robust medical segmentation

TL;DR

CATS v2 introduces a hybrid encoder architecture combining CNN and transformer paths with shifted window mechanisms, enhancing 3D medical image segmentation by leveraging local and global features for improved accuracy.

Contribution

This work extends the previous CATS model by integrating hybrid encoders with CNN and transformer components, improving segmentation robustness and accuracy across multiple datasets.

Findings

Outperforms state-of-the-art methods in Dice scores

Effective fusion of local and global features

Demonstrates robustness across diverse datasets

Abstract

Convolutional Neural Networks (CNNs) have exhibited strong performance in medical image segmentation tasks by capturing high-level (local) information, such as edges and textures. However, due to the limited field of view of convolution kernel, it is hard for CNNs to fully represent global information. Recently, transformers have shown good performance for medical image segmentation due to their ability to better model long-range dependencies. Nevertheless, transformers struggle to capture high-level spatial features as effectively as CNNs. A good segmentation model should learn a better representation from local and global features to be both precise and semantically accurate. In our previous work, we proposed CATS, which is a U-shaped segmentation network augmented with transformer encoder. In this work, we further extend this model and propose CATS v2 with hybrid encoders. Specifically, hybrid encoders consist of a CNN-based encoder path paralleled to a transformer path with a shifted window, which better leverage both local and global information to produce robust 3D medical image segmentation. We fuse the information from the convolutional encoder and the transformer at the skip connections of different resolutions to form the final segmentation. The proposed method is evaluated on three public challenge datasets: Beyond the Cranial Vault (BTCV), Cross-Modality Domain Adaptation (CrossMoDA) and task 5 of Medical Segmentation Decathlon (MSD-5), to segment abdominal organs, vestibular schwannoma (VS) and prostate, respectively. Compared with the state-of-the-art methods, our approach demonstrates superior performance in terms of higher Dice scores. Our code is publicly available at https://github.com/MedICL-VU/CATS.