HDFU-Net: An Improved Version of U-Net using a Hybrid Dice Focal Loss Function for Multi-modal Brain Tumor Image Segmentation
Islem Gammoudi, Raja Ghozi, Mohamed Ali Mahjoub · 2022
In the field of brain tumor image analysis, automatic brain tumor image segmentation remains a challenging task due to the varying sizes, shapes, and textures of type of these masses. Deep Learning algorithms have improved the speed and quality of segmentation for certain tasks in medical imaging, especially for Glioma image segmentation. This work present to design and evaluate an algorithm capable of segmenting MRI images. In this regard, U-Net is the most prominent deep network, we recall that it has been the most popular architecture in the medical image segmentation. In this paper, we propose modifications to the U-Net architecture in order efficiently handle a multi-modal MRI input. Based on these modifications, we develop a novel architecture, HDFU-Net as a potential successor to the U-Net architecture by introducing and presenting an improved dice loss that can address the demand for more accurate segmentation in medical images and modify network architecture to improve segmentation performance. The improved dice loss is called Hybrid Dice Focal loss (HDF loss). We then evaluate the presented approach on the BraTS 2020 dataset and discuss the results.