LCANet: A Lightweight Context-Aware Network for Bladder Tumor Segmentation in MRI Images

Abstract

Accurate segmentation of the lesion area from MRI images is essential for diagnosing bladder cancer. However, the precise segmentation of bladder tumors remains a massive challenge due to their similar intensity distributions, various tumor morphologies, and blurred boundaries. While some seminal studies, such as those using CNNs combined with transformer segmentation methods, have made significant progress, (1) how to reduce the computational complexity of the self-attention mechanism in the transformer while maintaining performance and (2) how to build a better global feature fusion process to improve segmentation performance still require further exploration. Considering the complexity of bladder MRI images, we developed a lightweight context-aware network (LCANet) to automatically segment bladder lesions from MRI images. Specifically, the local detail encoder generates local-level details of the lesion, the lightweight transformer encoder models the global-level features with different resolutions, the pyramid scene parsing module extracts high-level and multiscale semantic features, and the decoder provides high-resolution segmentation results by fusing local-level details with global-level cues at the channel level. A series of empirical studies on T2-weighted MRI images from 86 patients show that LCANet achieves an overall Jaccard index of 89.39%, a Dice similarity coefficient of 94.08%, and a Class pixel accuracy of 94.10%. These advantages show that our method is an efficient tool that can assist in reducing the heavy workload of radiologists.