Cascade Path Augmentation Unet for bladder cancer segmentation in MRI

Abstract

Treatment choices for patients with bladder cancer (BCa) are determined by the presence of muscular invasion. The precise segmentation of the inner and outer walls (IW and OW), as well as the bladder tumor (BT), is crucial for improving computer-aided diagnosis of muscle-invasive bladder cancer (MIBC). To propose a novel deep learning-based model to improve the segmentation accuracy of the IW, OW, and BT, which can be useful in clinical practice. We proposed a Cascade Path Augmentation Unet (CPA-Unet) network to conduct multi-regional segmentation of the bladder using 1545 T2-weighted MRI scans. The model employs a cascade strategy to eliminate the redundant information in the background. Unet is used to segment the bladder from the background in the rough segmentation. The path augmentation structure is used in the fine segmentation to mine multi-scale features. Additionally, the partial dense connection is adopted as the skip connection module to concatenate the low- and high-level sematic features. The CPA-Unet is trained using 1391 T2WI slices and tested using 154 T2WI slices. In comparison to previous deep learning-based methods, the CPA-Unet achieves superior segmentation results in terms of Dice similarity coefficient (DSC) and Hausdorff distance (HD) (IW: DSC =98.19%, HD =2.07mm; OW: DSC =82.24%, HD =2.62mm; BT: DSC =87.40%, HD =0.76mm). Our proposed CPA-Unet network is capable of segmenting the bladder into its IW and OW, as well as tumors. The segmentation results provide a reliable and effective foundation for computer-assisted clinical diagnosis of MIBC.