|
Glioblastoma is a highly malignant tumor. In recent years, many scholars have conducted research on the automatic segmentation of preoperative primary glioblastoma magnetic resonance imaging (MRI) and achieved good results. The automatic segmentation of postoperative residual glioblastoma MRI plays a crucial role in treatment planning. However, there is still no research specifically focusing on the segmentation of postoperative residual glioblastoma MRI due to the limited data and the difficulty in establishing standards. In this study, a large amount of preoperative tumor data was utilized to pretrain the segmentation model. Postoperative residual tumor MRI data from 53 patients were collected and annotated by medical students specializing in radiology. The pre-trained segmentation model was then applied to segment the postoperative residual tumor data, obtaining preliminary segmentation results that roughly indicate the location of the residual tumor. Based on the similarity between the preliminary segmentation results and the residual tumor annotations, a simple and effective active learning strategy is designed to select the cases that need to be reannotated. The preliminary segmentation results, along with the postoperative residual tumor data, were fed into a new segmentation network to achieve precise segmentation of the residual tumor after surgery. Ultimately, the proposed network achieved a Dice coefficient of 0.871 for the segmentation of residual tumor data.
|
