Review of MRI-based Brain Tumor Image Segmentation Using Deep Learning Methods

Işın, Ali
Direkoğlu, Cem
Şah, Melike
Brain tumor segmentation is an important task in medical image processing. Early diagnosis of brain tumors plays an important role in improving treatment possibilities and increases the survival rate of the patients. Manual segmentation of the brain tumors for cancer diagnosis, from large amount of MRI images generated in clinical routine, is a difficult and time consuming task. There is a need for automatic brain tumor image segmentation. The purpose of this paper is to provide a review of MRI-based brain tumor segmentation methods. Recently, automatic segmentation using deep learning methods proved popular since these methods achieve the state-of-the-art results and can address this problem better than other methods. Deep learning methods can also enable efficient processing and objective evaluation of the large amounts of MRI-based image data. There are number of existing review papers, focusing on traditional methods for MRI-based brain tumor image segmentation. Different than others, in this paper, we focus on the recent trend of deep learning methods in this field. First, an introduction to brain tumors and methods for brain tumor segmentation is given. Then, the state-of-the-art algorithms with a focus on recent trend of deep learning methods are discussed. Finally, an assessment of the current state is presented and future developments to standardize MRI-based brain tumor segmentation methods into daily clinical routine are addressed. (C) 2016 The Authors. Published by Elsevier B.V.


Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor Segmentation
Polat, Görkem; Işık Polat, Ece; Koçyiğit, Altan; Temizel, Alptekin (2021-9-27)
Availability of large, diverse, and multi-national datasets is crucial for the development of effective and clinically applicable AI systems in the medical imaging domain. However, forming a global model by bringing these datasets together at a central location, comes along with various data privacy and ownership problems. To alleviate these problems, several recent studies focus on the federated learning paradigm, a distributed learning approach for decentralized data. Federated learning leverages all the ...
Bayesian segmentation of human facial tissue using 3D MR-CT information fusion, resolution enhancement and partial volume modelling
Şener, Emre; Mumcuoğlu, Ünal Erkan; Hamcan, Salih (2016-02-01)
Background: Accurate segmentation of human head on medical images is an important process in a wide array of applications such as diagnosis, facial surgery planning, prosthesis design, and forensic identification.
Quality Enhancement of Computed Tomography Images of Porous Media Using Convolutional Neural Networks
Yıldırım, Ertuğrul Umut; Uğur, Ömür; Glatz, Guenther; Department of Scientific Computing (2022-2-11)
Computed tomography has been widely used in clinical and industrial applications as a non-destructive visualization technology. The quality of computed tomography scans has a strong effect on the accuracy of the estimated physical properties of the investigated sample. X-ray exposure time is a crucial factor for scan quality. Ideally, long exposure time scans, yielding large signal-to-noise ratios, are available if physical properties are to be delineated. However, especially in micro-computed tomography ap...
Automated cancer stem cell recognition in H&E stained tissue using convolutional neural networks and color deconvolution
Aichinger, Wolfgang; Krappe, Sebastian; ÇETİN, AHMET ENİS; Atalay, Rengül; ÜNER, AYŞEGÜL; Benz, Michaela; Wittenberg, Thomas; Stamminger, Marc; Muenzenmayer, Christian (2017-02-13)
The analysis and interpretation of histopathological samples and images is an important discipline in the diagnosis of various diseases, especially cancer. An important factor in prognosis and treatment with the aim of a precision medicine is the determination of so-called cancer stem cells (CSC) which are known for their resistance to chemotherapeutic treatment and involvement in tumor recurrence. Using immunohistochemistry with CSC markers like CD13, CD133 and others is one way to identify CSC. In our wor...
Mercadier, Deniz Sayin; Beşbınar, Beril; Frossard, Pascal (2019-01-01)
Accurate and fast segmentation of nuclei in histopathological images plays a crucial role in cancer research for detection and grading, as well as personal treatment. Despite the important efforts, current algorithms are still suboptimal in terms of speed, adaptivity and generalizability. Popular Deep Convolutional Neural Networks (DCNNs) have recently been utilized for nuclei segmentation, outperforming traditional approaches that exploit color and texture features in combination with shallow classifiers o...
Citation Formats
A. Işın, C. Direkoğlu, and M. Şah, “Review of MRI-based Brain Tumor Image Segmentation Using Deep Learning Methods,” 2016, vol. 102, p. 317, Accessed: 00, 2020. [Online]. Available: