  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.
