ECMS-NET:A multi-task model for early endometrial cancer MRI sequences classification and segmentation of key tumor structures

Longxiang Feng, Chunxia Chen, Lin Wang, Jiansong Zhang, Yapeng Li, Tiantian Yang, Yuling Fan, Peizhong Liu, Pengming Sun, Fang Huang

Abstract: Highlights•We delineate the case screening and dataset generation processes, along with offering guidance for visually inspecting MRI images in DICOM format outside hospital settings. Furthermore, we elaborate on the annotation and data-saving procedures required to create a compatible dataset for our computer model.•Due to the sequential nature of MRI images, we employ a classification approach to categorize them into two groups: images portraying tumors and those without. This foundational step is pivotal for subsequent segmentation experiments, crucial for achieving swift segmentation of image sequences.•Through meticulous consideration, we selected a segmentation model tailored to lesion characteristics and image features. This decision yielded our highest segmentation accuracy to date and offers potential support for imaging physicians in clinical diagnosis decisions.