Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge
Gliomas are the most common primary brain malignancies, with different degrees of
aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, ie …
aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, ie …
Magnetic resonance image-based brain tumour segmentation methods: A systematic review
JM Bhalodiya, SN Lim Choi Keung… - Digital Health, 2022 - journals.sagepub.com
Background Image segmentation is an essential step in the analysis and subsequent
characterisation of brain tumours through magnetic resonance imaging. In the literature …
characterisation of brain tumours through magnetic resonance imaging. In the literature …
MSMANet: A multi-scale mesh aggregation network for brain tumor segmentation
Y Zhang, Y Lu, W Chen, Y Chang, H Gu, B Yu - Applied Soft Computing, 2021 - Elsevier
The fine segmentation of brain tumor, which is instrumental in brain tumor diagnosis,
treatment planning and prognosis, is becoming a research hotspot in medical images …
treatment planning and prognosis, is becoming a research hotspot in medical images …
Evolutionary convolutional neural network for efficient brain tumor segmentation and overall survival prediction
The most common and aggressive malignant brain tumor in adults is glioma, which leads to
short life expectancy. A reliable and efficient automatic segmentation method is beneficial for …
short life expectancy. A reliable and efficient automatic segmentation method is beneficial for …
WLFS: Weighted label fusion learning framework for glioma tumor segmentation in brain MRI
Z Barzegar, M Jamzad - Biomedical Signal Processing and Control, 2021 - Elsevier
Glioma is a common type of tumor that develops in the brain. Due to many differences in the
shape and appearance, accurate segmentation of glioma for identifying all parts of the tumor …
shape and appearance, accurate segmentation of glioma for identifying all parts of the tumor …
Fully automated glioma tumour segmentation using anatomical symmetry plane detection in multimodal brain MRI
Z Barzegar, M Jamzad - IET Computer Vision, 2021 - Wiley Online Library
Automatic brain abnormality detection is a major challenge in medical image processing.
Manual lesion delineation techniques are susceptible to subjective errors, and therefore …
Manual lesion delineation techniques are susceptible to subjective errors, and therefore …
Modified MobileNet for patient survival prediction
Glioblastoma is a type of malignant tumor that varies significantly in size, shape, and
location. The study of this type of tumor, one of which is about predicting the patient's …
location. The study of this type of tumor, one of which is about predicting the patient's …
Brain tumor detection and segmentation from magnetic resonance image data using ensemble learning methods
The steadily growing amount of medical image data requires automatic segmentation
algorithms and decision support, because at a certain time, there will not be enough human …
algorithms and decision support, because at a certain time, there will not be enough human …
A reliable ensemble-based classification framework for glioma brain tumor segmentation
Z Barzegar, M Jamzad - Signal, Image and Video Processing, 2020 - Springer
Glioma is one of the most frequent primary brain tumors in adults that arise from glial cells.
Automatic and accurate segmentation of glioma is critical for detecting all parts of tumor and …
Automatic and accurate segmentation of glioma is critical for detecting all parts of tumor and …
Automatic segmentation of brain tumor parts from MRI data using a random forest classifier
The segmentation of brain tumor and the separation of its parts like the enhancing core or
edema represents a highly important problem, since a fine solution offers precise diagnosis …
edema represents a highly important problem, since a fine solution offers precise diagnosis …