Multiparametric MRI based deep learning model for automatic segmentation of tumor and lymph nodes in rectal cancer

Motivation: A significant challenge in RC management is the precise delineation of tumor boundaries and the accurate evaluation of LNs. The existing manual processes for this are time-consuming and subject to high variability. Goal(s): This study aimed to develop a deep learning approach for simultaneous RC tumor and LN segmentation. Approach: We constructed the model with mpMRI data input, ResUNet architecture, and focal cross entropy loss. Results: The ResUNet model achieved a mean SEN of 0.824, PRE of 0.619, and DSC of 0.694 in the validation dataset, indicating promising results. However, some false positives and false negatives were observed in LN segmentation. Impact: We introduced a ResUNet model for RC segmentation and achieved satisfactory results. While our findings are preliminary and may benefit from larger samples, this approach could improve tumor and LN segmentation and ultimately enhance clinical utility in RC management.