Automatic diagnostic system for segmentation of 3D/2D brain MRI images based on a hardware architecture

Hamdaoui, Fayçal; Sakly, Anis

doi:10.1016/j.micpro.2023.104814

Cited by 5 publications

(3 citation statements)

References 44 publications

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“…In Figure 3 , we show the number of MRI sequences used in the previous studies from Table 2 . According to this figure, the most used MRI sequence type is T1-weighted, and the sequence was used for different purposes such as segmentation [ 43 ] in 2023, image restoration in 2019 [ 52 ], reconstruction both in 2021 and 2022 [ 45 , 46 ], surface mapping in 2022 [ 44 ], and feature extraction in 2019 [ 54 ]. The second most used MRI sequence type is T2-weighted, and it was used for various tasks such as pulse sequence simulation in 2023 [ 42 ], reconstruction in 2021 [ 47 ], and simulation of a human head in 2021 [ 48 ].…”

Section: Discussionmentioning

confidence: 99%

“…The results of the simulation also showed the flexibility, reliability, and efficiency of the proposed framework. Another study by [ 43 ] aimed to propose an automated hardware architecture for the segmentation of MRI images in order to show differences in brain tissues. For this purpose, the authors used the Particle Swarm Optimization (PSO) algorithm in software GPU for improvements in velocity and position as well as fitness function.…”

Section: Literature Reviewmentioning

confidence: 99%

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GPU-Based Parallel Processing Techniques for Enhanced Brain Magnetic Resonance Imaging Analysis: A Review of Recent Advances

Kirimtat,

Krejcar

2024

Sensors

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The approach of using more than one processor to compute in order to overcome the complexity of different medical imaging methods that make up an overall job is known as GPU (graphic processing unit)-based parallel processing. It is extremely important for several medical imaging techniques such as image classification, object detection, image segmentation, registration, and content-based image retrieval, since the GPU-based parallel processing approach allows for time-efficient computation by a software, allowing multiple computations to be completed at once. On the other hand, a non-invasive imaging technology that may depict the shape of an anatomy and the biological advancements of the human body is known as magnetic resonance imaging (MRI). Implementing GPU-based parallel processing approaches in brain MRI analysis with medical imaging techniques might be helpful in achieving immediate and timely image capture. Therefore, this extended review (the extension of the IWBBIO2023 conference paper) offers a thorough overview of the literature with an emphasis on the expanding use of GPU-based parallel processing methods for the medical analysis of brain MRIs with the imaging techniques mentioned above, given the need for quicker computation to acquire early and real-time feedback in medicine. Between 2019 and 2023, we examined the articles in the literature matrix that include the tasks, techniques, MRI sequences, and processing results. As a result, the methods discussed in this review demonstrate the advancements achieved until now in minimizing computing runtime as well as the obstacles and problems still to be solved in the future.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

GPU-Based Parallel Processing Techniques for Enhanced Brain Magnetic Resonance Imaging Analysis: A Review of Recent Advances

Kirimtat,

Krejcar

2024

Sensors

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“…Brain tumors pose a substantial global health issue, as they have the potential to inflict extensive harm on the central nervous system [1][2], [3], [4]. Neoplasms, which are defined by the aberrant and unregulated proliferation of neural cells within the cranial cavity, provide a substantial risk to human survival.…”

Section: Introductionmentioning

confidence: 99%

Dual Vision Transformer-DSUNET With Feature Fusion for Brain Tumor Segmentation

Zakariah,

Al-Razgan,

Alfakih

2024

Preprint

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Brain tumors are one of the leading causes of cancer death; screening early is the best strategy to diagnose and treat brain tumors. Magnetic Resonance Imaging (MRI) is extensively utilized for brain tumor diagnosis; nevertheless, achieving improved accuracy and performance, which is a critical challenge in most of the previously reported automated medical diagnostics, is a difficult problem. The study introduces the Dual Vision Transformer-DSUNET model, which incorporates feature fusion techniques to provide precise and efficient differentiation between brain tumors and other brain regions by leveraging multi-modal MRI data. The impetus for this study arises from the necessity of automating the segmentation process of brain tumors in medical imaging, a critical component in the realms of diagnosis and therapy strategy. To tackle this issue the BRATS 2020 dataset is employed, an extensively utilized dataset for the segmentation of brain tumors. This dataset encompasses multi-modal MRI images, including T1-weighted, T2-weighted, T1Gd (contrast-enhanced), and FLAIR modalities. The proposed model incorporates the dual vision idea to comprehensively capture the heterogeneous properties of brain tumors across several imaging modalities. Moreover, the utilization of feature fusion techniques is implemented to augment the amalgamation of data originating from several modalities, hence enhancing the accuracy and dependability of tumor segmentation. The evaluation of the Dual Vision Transformer-DSUNET model's performance is conducted by employing the Dice Coefficient as a prevalent metric for quantifying segmentation accuracy. The results obtained from the experiment exhibit remarkable performance, with Dice Coefficient values of 91.47% for enhanced tumors, 92.38% for core tumors, and 90.88% for edema. The cumulative Dice score for the entirety of the classes is 91.29%. In addition, the model has a high level of accuracy, roughly 99.93%, which underscores its durability and efficacy in the task of segmenting brain tumors. Experimental findings demonstrate the integrity of the suggested architecture, which has quickly improved the detection accuracy of many brain diseases.

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