scholarly journals NI-07 VALIDATION OF MACHINE LEARNING BASED HIGH GRADE GLIOMA MR SEGMENTATION VIA METHIONINE PET

2019 ◽  
Vol 1 (Supplement_2) ◽  
pp. ii27-ii27
Author(s):  
Manabu Kinoshita ◽  
Tomohiko Ozaki ◽  
Hideyuki Arita ◽  
Naoki Kagawa ◽  
Yonehiro Kanemura ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Treatment Planning ◽  
Radiation Treatment ◽  
Ground Truth ◽  
Tumor Segmentation ◽  
High Grade ◽  
Lesion Segmentation ◽  
Brain Tumor Segmentation ◽  
Negative Error

Abstract Treatment planning and lesion-follow up are generally conducted by contrast-enhanced MRI in glioma patient care. On the other hand, there are, however, substantial concerns whether MRI actually reflects the extension or activity of this neoplasm, which information should be fundamentally important at every step when treating this disease. As a matter of fact, the authors of this investigation have already shown that there is no difference in tumor cell density within areas with and without contrast enhancement (J Neurosurg. 2016,125(5):1136–1142.) and furthermore that the geometry of MRI based-radiation treatment planning is significantly altered when methionine PET is integrated for this purpose (J Neurosurg. 2018 published on-line). Regardless of these concerns, there is great interest in the research community to construct a machine learning based fully automated brain tumor segmentation tool specific for gliomas using MRI. The authors attempted to validate this method by comparing MRI-based automated brain tumor segmentation and methionine PET. Consecutively collected 45 high-grade gliomas (GBM-26, grade3-19) were analyzed. BraTumIA, an automated brain tumor segmentation tool, was used for machine learning based lesion segmentation. At the same time, lesions were segmented using various thresholds on methionine PET. The authors observed 40% of pseudo-positive and 90% of pseudo-negative error on BraTumIA based lesion segmentation when methionine PET was considered as ground truth with a cut-off of 1.3 in T/N ratio. Pseudo-negative error was as high as 60% even if the threshold was elevated to 2.0. Although machine learning based glioma segmentation is expected to expand in both research and clinical use, the observed results caution the use of MRI as ground truth of spatial extension of glioma and researchers should be reminded that this imaging modality may obscure the true behavior of the disease within the patient in some cases.

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 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.

HYBRID APPROACH FOR BRAIN TUMOR SEGMENTATION IN MAGNETIC RESONANCE IMAGES USING CELLULAR NEURAL NETWORKS AND OPTIMIZATION TECHNIQUES

2010 ◽  
Vol 09 (01) ◽  
pp. 17-31 ◽  
Author(s):  
D. SELVATHI ◽  
HENRY SELVARAJ ◽  
S. THAMARAI SELVI
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Brain Tumor ◽  
Simulated Annealing ◽  
Magnetic Resonance ◽  
Ground Truth ◽  
Magnetic Resonance Images ◽  
Optimization Techniques ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation

Tumor segmentation from brain magnetic resonance image data is an important but time consuming task performed manually by medical experts. Automating this process is challenging due to the high diversity in appearance of tumor tissue among different patients and in many cases, similarity between tumor and normal tissue. This paper deals with an efficient segmentation algorithm for extracting brain tumors in magnetic resonance images using Cellular Neural Networks (CNN). Learning CNN templates values are formulated as an optimization problem. The template coefficients (weights) of an CNN which will give a desired performance, can be derived by learning genetic algorithm and simulated annealing optimization techniques. The objective of this work is to compare the performance of genetic algorithm (GA) and simulated annealing (SA) for finding the optimum template values in the CNN which is used for segmenting the tumor region in the abnormal MR images. The method is applied on real data of MRI images of thirty patients with four different types of tumors. The results are compared with radiologist labeled ground truth. Quantitative analysis between ground truth and segmented tumor is presented in terms of segmentation efficiency. From the analysis and performance measures like segmentation accuracy, it is inferred that the brain tumor segmentation is best done using CNN with genetic algorithm template optimization than CNN with simulated annealing template optimization. An average accuracy rate of above 95% was obtained using this segmentation algorithm.

scholarly journals Computerized brain tumor segmentation in magnetic resonance imaging

2012 ◽  
Vol 10 (2) ◽  
pp. 158-163 ◽  
Author(s):  
Maryana de Carvalho Alegro ◽  
Edson Amaro Junior ◽  
Rosei de Deus Lopes
Keyword(s):  
Brain Tumor ◽  
User Interaction ◽  
Ground Truth ◽  
Tumor Segmentation ◽  
Correct Match ◽  
Resonance Imaging ◽  
Brain Tumor Segmentation ◽  
Texture Characteristics ◽  
The Mean ◽  
Source Of Information

OBJECTIVE: To propose an automatic brain tumor segmentation system. METHODS: The system used texture characteristics as its main source of information for segmentation. RESULTS: The mean correct match was 94% of correspondence between the segmented areas and ground truth. CONCLUSION: Final results showed that the proposed system was able to find and delimit tumor areas without requiring any user interaction.

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