U-Net-Based Models towards Optimal MR Brain Image Segmentation

Abstract

Brain tumor segmentation from MRIs has always been a challenging task for radiologists, therefore, an automatic and generalized system to address this task is needed. Among all other deep learning techniques used in medical imaging, U-Net-based variants are the most used models found in the literature to segment medical images with respect to different modalities. Therefore, the goal of this paper is to examine the numerous advancements and innovations in the U-Net architecture, as well as recent trends, with the aim of highlighting the ongoing potential of U-Net being used to better the performance of brain tumor segmentation. Furthermore, we provide a quantitative comparison of different U-Net architectures to highlight the performance and the evolution of this network from an optimization perspective. In addition to that, we have experimented with four U-Net architectures (3D U-Net, Attention U-Net, R2 Attention U-Net, and modified 3D U-Net) on the BraTS 2020 dataset for brain tumor segmentation to provide a better overview of this architecture’s performance in terms of Dice score and Hausdorff distance 95%. Finally, we analyze the limitations and challenges of medical image analysis to provide a critical discussion about the importance of developing new architectures in terms of optimization.

Effectiveness of a diagnostic algorithm for dengue based on an artificial neural network

“…Likewise, another strength apart from having a standardized criterion for the study of people with suspected dengue is that the cases were included directly from the epidemiological surveillance system after validation of the operational personnel responsible for epidemiological surveillance. Although there are other algorithms that have been used in medical diagnosis with a good level of accuracy when compared with traditional machine learning and volumetric techniques 26 ; all of them have focused on images (radiological 27 or histopathological 28 ); based on Convolutional Neural Network 26 or dual-path network. 28 In the present study, data from various sources of information (clinical records and epidemiological surveillance systems) were used, which is why an ANN was used in the study.…”

Bridged-U-Net-ASPP-EVO and Deep Learning Optimization for Brain Tumor Segmentation

“…Luu et al [ 49 ] (who slightly outperformed our proposed model ) have used the nnU-Net approach, which was found to be very time consuming due to the extensive fine tuning DL components (hyperparameters, optimizers, activations, etc.) [ 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ]; moreover, recent optimizers and loss functions need to be implemented manually because these two elements are fixed in their original model. Moreover, the proposed model can be efficiently used for other applications of deep learning in medical image segmentation other than in brain tumors.…”

Feature fusion method for pulmonary tuberculosis patient detection based on cough sound

“…Sound can reflect changes in lung tissues, organs, bronchus secretions, and carry signals related to lung and respiratory abnormalities. Deep learning has been widely used in medical-related classification tasks, particularly in the classification of tumors [ 11 , 12 ]. The diagnosis of various types and severities of respiratory diseases can be facilitated by analyzing autonomous or stimulated human sounds, such as breathing and coughing [ 13 ].…”