scholarly journals Towards Reinforced Brain Tumor Segmentation on MRI Images Based on Temperature Changes on Pathologic Area

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Abdelmajid Bousselham ◽  
Omar Bouattane ◽  
Mohamed Youssfi ◽  
Abdelhadi Raihani
Keyword(s):  
Brain Tumor ◽  
Level Set ◽  
False Negative ◽  
Ground Truth ◽  
Tumor Segmentation ◽  
Normal Brain ◽  
Bioheat Equation ◽  
Brain Tumor Segmentation ◽  
Temperature Changes ◽  
Negative Results

Brain tumor segmentation is the process of separating the tumor from normal brain tissues; in clinical routine, it provides useful information for diagnosis and treatment planning. However, it is still a challenging task due to the irregular form and confusing boundaries of tumors. Tumor cells thermally represent a heat source; their temperature is high compared to normal brain cells. The main aim of the present paper is to demonstrate that thermal information of brain tumors can be used to reduce false positive and false negative results of segmentation performed in MRI images. Pennes bioheat equation was solved numerically using the finite difference method to simulate the temperature distribution in the brain; Gaussian noises of ±2% were added to the simulated temperatures. Canny edge detector was used to detect tumor contours from the calculated thermal map, as the calculated temperature showed a large gradient in tumor contours. The proposed method is compared to Chan–Vese based level set segmentation method applied to T1 contrast-enhanced and Flair MRI images of brains containing tumors with ground truth. The method is tested in four different phantom patients by considering different tumor volumes and locations and 50 synthetic patients taken from BRATS 2012 and BRATS 2013. The obtained results in all patients showed significant improvement using the proposed method compared to segmentation by level set method with an average of 0.8% of the tumor area and 2.48% of healthy tissue was differentiated using thermal images only. We conclude that tumor contours delineation based on tumor temperature changes can be exploited to reinforce and enhance segmentation algorithms in MRI diagnostic.

A level set method based on domain transformation and bias correction for MRI brain tumor segmentation

2021 ◽  
Vol 352 ◽  
pp. 109091
Author(s):  
Asieh Khosravanian ◽  
Mohammad Rahmanimanesh ◽  
Parviz Keshavarzi ◽  
Saeed Mozaffari
Keyword(s):  
Brain Tumor ◽  
Level Set Method ◽  
Level Set ◽  
Bias Correction ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Mri Brain ◽  
Domain Transformation

Advancements of MRI-Based Brain Tumor Segmentation from Traditional to Recent Trends- A Review

2021 ◽  
Vol 17 ◽  
Author(s):  
Padmapriya Thiyagarajan ◽  
Sriramakrishnan Padmanaban ◽  
Kalaiselvi Thiruvenkadam ◽  
Somasundaram Karuppanagounder
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Brain Tumor ◽  
Human Head ◽  
Medical Community ◽  
Tumor Segmentation ◽  
Normal Brain ◽  
Learning Methods ◽  
Brain Tumor Segmentation ◽  
Mri Scans

Background: Among the brain-related diseases, brain tumor segmentation on magnetic resonance imaging (MRI) scans is one of the highly focused research domains in the medical community. Brain tumor segmentation is a very challenging task due to its asymmetric form and uncertain boundaries. This process segregates the tumor region into the active tumor, necrosis and edema from normal brain tissues such as white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF). Introduction: The proposed paper analyzed the advancement of brain tumor segmentation from conventional image processing techniques, to deep learning through machine learning on MRI of human head scans. Method: State-of-the-art methods of these three techniques are investigated, and the merits and demerits are discussed. Results: The prime motivation of the paper is to instigate the young researchers towards the development of efficient brain tumor segmentation techniques using conventional and recent technologies. Conclusion: The proposed analysis concluded that the conventional and machine learning methods were mostly applied for brain tumor detection, whereas deep learning methods were good at tumor substructures segmentation.

scholarly journals Brain Tumor Segmentation via Multi-Modalities Interactive Feature Learning

2021 ◽  
Vol 8 ◽  
Author(s):  
Bo Wang ◽  
Jingyi Yang ◽  
Hong Peng ◽  
Jingyang Ai ◽  
Lihua An ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Feature Learning ◽  
Image Data ◽  
Brain Regions ◽  
Tumor Segmentation ◽  
Normal Brain ◽  
Brain Tumor Segmentation ◽  
Learning Framework ◽  
Interactive Feature

Automatic segmentation of brain tumors from multi-modalities magnetic resonance image data has the potential to enable preoperative planning and intraoperative volume measurement. Recent advances in deep convolutional neural network technology have opened up an opportunity to achieve end-to-end segmenting the brain tumor areas. However, the medical image data used in brain tumor segmentation are relatively scarce and the appearance of brain tumors is varied, so that it is difficult to find a learnable pattern to directly describe tumor regions. In this paper, we propose a novel cross-modalities interactive feature learning framework to segment brain tumors from the multi-modalities data. The core idea is that the multi-modality MR data contain rich patterns of the normal brain regions, which can be easily captured and can be potentially used to detect the non-normal brain regions, i.e., brain tumor regions. The proposed multi-modalities interactive feature learning framework consists of two modules: cross-modality feature extracting module and attention guided feature fusing module, which aim at exploring the rich patterns cross multi-modalities and guiding the interacting and the fusing process for the rich features from different modalities. Comprehensive experiments are conducted on the BraTS 2018 benchmark, which show that the proposed cross-modality feature learning framework can effectively improve the brain tumor segmentation performance when compared with the baseline methods and state-of-the-art methods.

scholarly journals BRAIN TUMOR SEGMENTATION BASED ON U-NET WITH IMAGE DRIVEN LEVEL SET LOSS

2021 ◽  
Vol 59 (5) ◽  
Author(s):  
Truong Van Pham ◽  
Thao Thi Tran
Keyword(s):  
Brain Tumor ◽  
Level Set ◽  
Loss Function ◽  
High Performance ◽  
Deep Neural Networks ◽  
Brain Mri ◽  
Tumor Segmentation ◽  
Dice Coefficient ◽  
Brain Tumor Segmentation ◽  
Mri Scans

This paper presents an approach for brain tumor segmentation based on deep neural networks. The paper proposes to utilize U-Net as an architecture of the approach to capture the fine and soars information from input images. Especially, to train the network, instead of using commonly used cross-entropy loss, dice loss or both, in this study, we propose to employ a new loss function including Level set loss and Dice loss function. The level set loss is inspired from Mumford-Shah functional for unsupervised task. Meanwhile, the Dice loss function measures the similarity between the predicted mask and desired mask. The proposed approach is then applied to segment brain tumor from MRI images as well as evaluated and compared with other approaches on a dataset of nearly 4000 brain MRI scans. Experiment results show that the proposed approach achieves high performance in terms of Dice coefficient and Intersection over Union (IoU) scores.

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