Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images

Abstract

Brain tumor localization and segmentation from magnetic resonance imaging (MRI) are hard and important tasks for several applications in the field of medical analysis. As each brain imaging modality gives unique and key details related to each part of the tumor, many recent approaches used four modalities T1, T1c, T2, and FLAIR. Although many of them obtained a promising segmentation result on the BRATS 2018 dataset, they suffer from a complex structure that needs more time to train and test. So, in this paper, to obtain a flexible and effective brain tumor segmentation system, first, we propose a preprocessing approach to work only on a small part of the image rather than the whole part of the image. This method leads to a decrease in computing time and overcomes the overfitting problems in a Cascade Deep Learning model. In the second step, as we are dealing with a smaller part of brain images in each slice, a simple and efficient Cascade Convolutional Neural Network (C-ConvNet/C-CNN) is proposed. This C-CNN model mines both local and global features in two different routes. Also, to improve the brain tumor segmentation accuracy compared with the state-of-the-art models, a novel Distance-Wise Attention (DWA) mechanism is introduced. The DWA mechanism considers the effect of the center location of the tumor and the brain inside the model. Comprehensive experiments are conducted on the BRATS 2018 dataset and show that the proposed model obtains competitive results: the proposed method achieves a mean whole tumor, enhancing tumor, and tumor core dice scores of 0.9203, 0.9113 and 0.8726 respectively. Other quantitative and qualitative assessments are presented and discussed.

Automatic Segmentation of Pelvic Cancers Using Deep Learning: State-of-the-Art Approaches and Challenges

“…The industry/application-specific requirements continually encourage innovation and the development of sophisticated networks. The future outlook for pelvic cancer segmentation may include intricate knowledge transfer from pre-trained models on very large datasets or perhaps adaption of key developments from non-medical applications [181] or ones not yet configured for the pelvis [182,183]. The examples of this may include explainable/interpretable AI, domain adaptation and continuous and/or federated learning.…”

Polish Multi-Institutional Study of Children with Ependymoma—Clinical Practice Outcomes in the Light of Prospective Trials

“…The use of artificial intelligence and deep learning is a matter of discussion in recent years. It could help not only in the detection process and the classification into benign and malignant types, but also during the follow-up of the patient, and could be of value in terms of RTH and tumour and healthy tissue delineation [ 80 , 81 , 82 , 83 , 84 , 85 ]. Very high accuracy was reported for many of those systems that could improve the diagnostic process in the future and that have the potential to find tumour regions with the most aggressive tissues [ 85 ].…”

An Extended Approach to Predict Retinopathy in Diabetic Patients Using the Genetic Algorithm and Fuzzy C-Means

“…In recent years, image processing has been extensively used, especially with the advent of advanced techniques such as discriminatory information processing, e.g., digital cameras and scanners. On the other hand, the images resulted from these techniques are generally associated with different degrees of noise, and even sometimes, these techniques fail to fade boundary inside an image [ 33 , 41 – 43 ]. This problem finally decreases the resultant image resolution.…”