Image segmentation is an essential research field in image processing that has developed from traditional processing techniques to modern deep learning methods. In medical image processing, the primary goal of the segmentation process is to segment organs, lesions or tumors. Segmentation of tumors in the brain is a difficult task due to the vast variations in the intensity and size of gliomas. Clinical segmentation typically requires a high-quality image with relevant features and domain experts for the best results. Due to this, automatic segmentation is a necessity in modern society since gliomas are considered highly malignant. Encoder-decoder-based structures, as popular as they are, have some areas where the research is still in progress, like reducing the number of false positives and false negatives. Sometimes these models also struggled to capture the finest boundaries, producing jagged or inaccurate boundaries after segmentation. This research article introduces a novel and efficient method for segmenting out the tumorous region in brain images to overcome the research gap of the recent state-of-the-art deep learning-based segmentation approaches. The proposed 4-staged 2D-VNET + + is an efficient deep learning tumor segmentation network that introduces a context-boosting framework and a custom loss function to accomplish the task. The results show that the proposed model gives a Dice score of 99.287, Jaccard similarity index of 99.642 and a Tversky index of 99.743, all of which outperform the recent state-of-the-art techniques like 2D-VNet, Attention ResUNet with Guided Decoder (ARU-GD), MultiResUNet, 2D UNet, Link Net, TransUNet and 3D-UNet.
Paramanandham et al. (Tue,) studied this question.