CATS v2: Hybrid encoders for robust medical segmentation
This work addresses the need for precise and semantically accurate segmentation in medical imaging, though it is incremental as it builds on their previous CATS model.
The authors tackled the problem of robust 3D medical image segmentation by proposing CATS v2, a hybrid encoder model that combines CNNs and transformers to leverage both local and global features, resulting in superior performance with higher Dice scores on three public datasets.
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.