Manual delineation of tumor regions from magnetic resonance (MR) images is time-consuming, requires an expert, and is prone to human error. In recent years, deep learning models have been the go-to approach for the segmentation of brain tumors. U-Net and its' variants for semantic segmentation of medical images have achieved good results in the literature. However, U-Net and its' variants tend to over-segment tumor regions and may not accurately segment the tumor edges. The edges of the tumor are as important as the tumor regions for accurate diagnosis, surgical precision, and treatment planning. In the proposed work, the authors aim to extract edges from the ground truth using a derivative-like filter followed by edge reconstruction to obtain an edge ground truth in addition to the brain tumor ground truth. Utilizing both ground truths, the author studies several U-Net and its' variant architectures with and without tumor edges ground truth as a target along with the tumor ground truth for brain tumor segmentation. The author used the BraTS2020 benchmark dataset to perform the study and the results are tabulated for the dice and Hausdorff95 metrics. The mean and median metrics are calculated for the whole tumor (WT), tumor core (TC), and enhancing tumor (ET) regions. Compared to the baseline U-Net and its variants, the models that learned edges along with the tumor regions performed well in core tumor regions in both training and validation datasets. The improved performance of edge-trained models trained on baseline models like U-Net and V-Net achieved performance similar to baseline state-of-the-art models like Swin U-Net and hybrid MR-U-Net. The edge-target trained models are capable of generating edge maps that can be useful for treatment planning. Additionally, for further explainability of the results, the activation map generated by the hybrid MR-U-Net has been studied.

通过使用边缘地面真实度的方法，与基线U-Net和其变种相比，边缘目标训练的模型在核心瘤区域表现良好，并且获得与Swin U-Net和混合MR-U-Net等基线最先进模型相似的性能，这些边缘训练的模型能够生成对于治疗规划有用的边缘图。此外，为了进一步解释结果，研究了混合MR-U-Net生成的激活图。

将边缘融入U-Net模型并使用可解释的激活图进行脑肿瘤分割，基于MR图像