1
|
Zhang W, Tao Y, Huang Z, Li Y, Chen Y, Song T, Ma X, Zhang Y. Multi-phase features interaction transformer network for liver tumor segmentation and microvascular invasion assessment in contrast-enhanced CT. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:5735-5761. [PMID: 38872556 DOI: 10.3934/mbe.2024253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
Precise segmentation of liver tumors from computed tomography (CT) scans is a prerequisite step in various clinical applications. Multi-phase CT imaging enhances tumor characterization, thereby assisting radiologists in accurate identification. However, existing automatic liver tumor segmentation models did not fully exploit multi-phase information and lacked the capability to capture global information. In this study, we developed a pioneering multi-phase feature interaction Transformer network (MI-TransSeg) for accurate liver tumor segmentation and a subsequent microvascular invasion (MVI) assessment in contrast-enhanced CT images. In the proposed network, an efficient multi-phase features interaction module was introduced to enable bi-directional feature interaction among multiple phases, thus maximally exploiting the available multi-phase information. To enhance the model's capability to extract global information, a hierarchical transformer-based encoder and decoder architecture was designed. Importantly, we devised a multi-resolution scales feature aggregation strategy (MSFA) to optimize the parameters and performance of the proposed model. Subsequent to segmentation, the liver tumor masks generated by MI-TransSeg were applied to extract radiomic features for the clinical applications of the MVI assessment. With Institutional Review Board (IRB) approval, a clinical multi-phase contrast-enhanced CT abdominal dataset was collected that included 164 patients with liver tumors. The experimental results demonstrated that the proposed MI-TransSeg was superior to various state-of-the-art methods. Additionally, we found that the tumor mask predicted by our method showed promising potential in the assessment of microvascular invasion. In conclusion, MI-TransSeg presents an innovative paradigm for the segmentation of complex liver tumors, thus underscoring the significance of multi-phase CT data exploitation. The proposed MI-TransSeg network has the potential to assist radiologists in diagnosing liver tumors and assessing microvascular invasion.
Collapse
Affiliation(s)
- Wencong Zhang
- Department of Biomedical Engineering, College of Engineering, Shantou University, Shantou, China
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
| | - Yuxi Tao
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Zhanyao Huang
- Department of Biomedical Engineering, College of Engineering, Shantou University, Shantou, China
| | - Yue Li
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Yingjia Chen
- Department of Biomedical Engineering, College of Engineering, Shantou University, Shantou, China
| | - Tengfei Song
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China
| | - Xiangyuan Ma
- Department of Biomedical Engineering, College of Engineering, Shantou University, Shantou, China
| | - Yaqin Zhang
- Department of Biomedical Engineering, College of Engineering, Shantou University, Shantou, China
| |
Collapse
|
2
|
Ahamed MF, Hossain MM, Nahiduzzaman M, Islam MR, Islam MR, Ahsan M, Haider J. A review on brain tumor segmentation based on deep learning methods with federated learning techniques. Comput Med Imaging Graph 2023; 110:102313. [PMID: 38011781 DOI: 10.1016/j.compmedimag.2023.102313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 11/13/2023] [Accepted: 11/13/2023] [Indexed: 11/29/2023]
Abstract
Brain tumors have become a severe medical complication in recent years due to their high fatality rate. Radiologists segment the tumor manually, which is time-consuming, error-prone, and expensive. In recent years, automated segmentation based on deep learning has demonstrated promising results in solving computer vision problems such as image classification and segmentation. Brain tumor segmentation has recently become a prevalent task in medical imaging to determine the tumor location, size, and shape using automated methods. Many researchers have worked on various machine and deep learning approaches to determine the most optimal solution using the convolutional methodology. In this review paper, we discuss the most effective segmentation techniques based on the datasets that are widely used and publicly available. We also proposed a survey of federated learning methodologies to enhance global segmentation performance and ensure privacy. A comprehensive literature review is suggested after studying more than 100 papers to generalize the most recent techniques in segmentation and multi-modality information. Finally, we concentrated on unsolved problems in brain tumor segmentation and a client-based federated model training strategy. Based on this review, future researchers will understand the optimal solution path to solve these issues.
Collapse
Affiliation(s)
- Md Faysal Ahamed
- Department of Computer Science & Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Md Munawar Hossain
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Md Nahiduzzaman
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Md Rabiul Islam
- Department of Computer Science & Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Md Robiul Islam
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Mominul Ahsan
- Department of Computer Science, University of York, Deramore Lane, Heslington, York YO10 5GH, UK
| | - Julfikar Haider
- Department of Engineering, Manchester Metropolitan University, Chester St, Manchester M1 5GD, UK.
| |
Collapse
|
3
|
Zhou T, Zhu S. Uncertainty quantification and attention-aware fusion guided multi-modal MR brain tumor segmentation. Comput Biol Med 2023; 163:107142. [PMID: 37331100 DOI: 10.1016/j.compbiomed.2023.107142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 05/17/2023] [Accepted: 06/05/2023] [Indexed: 06/20/2023]
Abstract
Brain tumor is one of the most aggressive cancers in the world, accurate brain tumor segmentation plays a critical role in clinical diagnosis and treatment planning. Although deep learning models have presented remarkable success in medical segmentation, they can only obtain the segmentation map without capturing the segmentation uncertainty. To achieve accurate and safe clinical results, it is necessary to produce extra uncertainty maps to assist the subsequent segmentation revision. To this end, we propose to exploit the uncertainty quantification in the deep learning model and apply it to multi-modal brain tumor segmentation. In addition, we develop an effective attention-aware multi-modal fusion method to learn the complimentary feature information from the multiple MR modalities. First, a multi-encoder-based 3D U-Net is proposed to obtain the initial segmentation results. Then, an estimated Bayesian model is presented to measure the uncertainty of the initial segmentation results. Finally, the obtained uncertainty maps are integrated into a deep learning-based segmentation network, serving as an additional constraint information to further refine the segmentation results. The proposed network is evaluated on publicly available BraTS 2018 and BraTS 2019 datasets. The experimental results demonstrate that the proposed method outperforms the previous state-of-the-art methods on Dice score, Hausdorff distance and Sensitivity metrics. Furthermore, the proposed components could be easily applied to other network architectures and other computer vision fields.
Collapse
Affiliation(s)
- Tongxue Zhou
- School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China
| | - Shan Zhu
- School of Life and Environmental Science, Hangzhou Normal University, Hangzhou, 311121, China.
| |
Collapse
|
4
|
Modality-level cross-connection and attentional feature fusion based deep neural network for multi-modal brain tumor segmentation. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
|
5
|
Zhou T, Ruan S, Hu H. A literature survey of MR-based brain tumor segmentation with missing modalities. Comput Med Imaging Graph 2023; 104:102167. [PMID: 36584536 DOI: 10.1016/j.compmedimag.2022.102167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 11/01/2022] [Accepted: 12/22/2022] [Indexed: 12/28/2022]
Abstract
Multimodal MR brain tumor segmentation is one of the hottest issues in the community of medical image processing. However, acquiring the complete set of MR modalities is not always possible in clinical practice, due to the acquisition protocols, image corruption, scanner availability, scanning cost or allergies to certain contrast materials. The missing information can cause some restraints to brain tumor diagnosis, monitoring, treatment planning and prognosis. Thus, it is highly desirable to develop brain tumor segmentation methods to address the missing modalities problem. Based on the recent advancements, in this review, we provide a detailed analysis of the missing modality issue in MR-based brain tumor segmentation. First, we briefly introduce the biomedical background concerning brain tumor, MR imaging techniques, and the current challenges in brain tumor segmentation. Then, we provide a taxonomy of the state-of-the-art methods with five categories, namely, image synthesis-based method, latent feature space-based model, multi-source correlation-based method, knowledge distillation-based method, and domain adaptation-based method. In addition, the principles, architectures, benefits and limitations are elaborated in each method. Following that, the corresponding datasets and widely used evaluation metrics are described. Finally, we analyze the current challenges and provide a prospect for future development trends. This review aims to provide readers with a thorough knowledge of the recent contributions in the field of brain tumor segmentation with missing modalities and suggest potential future directions.
Collapse
Affiliation(s)
- Tongxue Zhou
- School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China
| | - Su Ruan
- Université de Rouen Normandie, LITIS - QuantIF, Rouen 76183, France
| | - Haigen Hu
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China; Key Laboratory of Visual Media Intelligent Processing Technology of Zhejiang Province, Hangzhou 310023, China.
| |
Collapse
|
6
|
Cao Y, Zhou W, Zang M, An D, Feng Y, Yu B. MBANet: A 3D convolutional neural network with multi-branch attention for brain tumor segmentation from MRI images. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
|
7
|
Qureshi I, Yan J, Abbas Q, Shaheed K, Riaz AB, Wahid A, Khan MWJ, Szczuko P. Medical image segmentation using deep semantic-based methods: A review of techniques, applications and emerging trends. INFORMATION FUSION 2023. [DOI: 10.1016/j.inffus.2022.09.031] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
|
8
|
Lv P, Yang J, Wang J, Guo Y, Tang Q, Magnier B, Lin J, Zhou J. Ischemic stroke prediction of patients with carotid atherosclerotic stenosis via multi-modality fused network. Front Neurosci 2023; 17:1118376. [PMID: 36908778 PMCID: PMC9998529 DOI: 10.3389/fnins.2023.1118376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 02/06/2023] [Indexed: 03/14/2023] Open
Abstract
Carotid atherosclerotic stenosis of the carotid artery is an important cause of ischemic cerebrovascular disease. The aim of this study was to predict the presence or absence of clinical symptoms in unknown patients by studying the existence or lack of symptoms of patients with carotid atherosclerotic stenosis. First, a deep neural network prediction model based on brain MRI imaging data of patients with multiple modalities is constructed; it uses the multi-modality features extracted from the neural network as inputs and the incidence of diagnosis as output to train the model. Then, a machine learning-based classification algorithm is developed to utilize the clinical features for comparison and evaluation. The experimental results showed that the deep learning model using imaging data could better predict the clinical symptom classification of patients. As part of preventive medicine, this study could help patients with carotid atherosclerosis narrowing to prepare for stroke prevention based on the prediction results.
Collapse
Affiliation(s)
- Peng Lv
- Department of Radiology, Zhongshan Hospital, Fudan University and Shanghai Institute of Medical Imaging, Shanghai, China
| | - Jing Yang
- School of Medicine, Xiamen University, Xiamen, China
| | - Jiacheng Wang
- Department of Computer Science at School of Informatics, Xiamen University, Xiamen, China
| | - Yi Guo
- Department of Radiology, Zhongshan Hospital Xiamen, Fudan University, Xiamen, China.,Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China
| | - Qiying Tang
- Department of Radiology, Zhongshan Hospital Xiamen, Fudan University, Xiamen, China.,Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China
| | - Baptiste Magnier
- Euromov Digital Health in Motion, Univ Montpellier, IMT Mines Ales, Ales, France
| | - Jiang Lin
- Department of Radiology, Zhongshan Hospital, Fudan University and Shanghai Institute of Medical Imaging, Shanghai, China
| | - Jianjun Zhou
- Department of Radiology, Zhongshan Hospital Xiamen, Fudan University, Xiamen, China.,Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China
| |
Collapse
|
9
|
Zhu R, Zou H, Li Z, Ni R. Apple-Net: A Model Based on Improved YOLOv5 to Detect the Apple Leaf Diseases. PLANTS (BASEL, SWITZERLAND) 2022; 12:plants12010169. [PMID: 36616300 PMCID: PMC9824080 DOI: 10.3390/plants12010169] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 12/25/2022] [Accepted: 12/27/2022] [Indexed: 05/27/2023]
Abstract
Effective identification of apple leaf diseases can reduce pesticide spraying and improve apple fruit yield, which is significant to agriculture. However, the existing apple leaf disease detection models lack consideration of disease diversity and accuracy, which hinders the application of intelligent agriculture in the apple industry. In this paper, we explore an accurate and robust detection model for apple leaf disease called Apple-Net, improving the conventional YOLOv5 network by adding the Feature Enhancement Module (FEM) and Coordinate Attention (CA) methods. The combination of the feature pyramid and pan in YOLOv5 can obtain richer semantic information and enhance the semantic information of low-level feature maps but lacks the output of multi-scale information. Thus, the FEM was adopted to improve the output of multi-scale information, and the CA was used to improve the detection efficiency. The experimental results show that Apple-Net achieves a higher mAP@0.5 (95.9%) and precision (93.1%) than four classic target detection models, thus proving that Apple-Net achieves more competitive results on apple leaf disease identification.
Collapse
|
10
|
Pan X, Xu J, Pan Y, Wen L, Lin W, Bai K, Fu H, Xu Z. AFINet: Attentive Feature Integration Networks for image classification. Neural Netw 2022; 155:360-368. [PMID: 36115162 DOI: 10.1016/j.neunet.2022.08.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 07/03/2022] [Accepted: 08/28/2022] [Indexed: 10/31/2022]
Abstract
Convolutional Neural Networks (CNNs) have achieved tremendous success in a number of learning tasks including image classification. Residual-like networks, such as ResNets, mainly focus on the skip connection to avoid gradient vanishing. However, the skip connection mechanism limits the utilization of intermediate features due to simple iterative updates. To mitigate the redundancy of residual-like networks, we design Attentive Feature Integration (AFI) modules, which are widely applicable to most residual-like network architectures, leading to new architectures named AFI-Nets. AFI-Nets explicitly model the correlations among different levels of features and selectively transfer features with a little overhead. AFI-ResNet-152 obtains a 1.24% relative improvement on the ImageNet dataset while decreases the FLOPs by about 10% and the number of parameters by about 9.2% compared to ResNet-152.
Collapse
Affiliation(s)
- Xinglin Pan
- University of Electronic Science and Technology of China, Chengdu, China; Department of Network Intelligence, Peng Cheng Lab, Shenzhen, China
| | - Jing Xu
- School of Science and Technology, Harbin Institute of Technology Shenzhen, Shenzhen, China; Department of Network Intelligence, Peng Cheng Lab, Shenzhen, China
| | - Yu Pan
- School of Science and Technology, Harbin Institute of Technology Shenzhen, Shenzhen, China
| | - Liangjian Wen
- University of Electronic Science and Technology of China, Chengdu, China
| | | | - Kun Bai
- Cloud and Smart Industries Group, Tencent, China
| | - Hongguang Fu
- University of Electronic Science and Technology of China, Chengdu, China.
| | - Zenglin Xu
- School of Science and Technology, Harbin Institute of Technology Shenzhen, Shenzhen, China; Department of Network Intelligence, Peng Cheng Lab, Shenzhen, China; Key Laboratory of Novel Security Intelligence Technologies, Shenzhen, China.
| |
Collapse
|
11
|
Research and Analysis of Brain Glioma Imaging Based on Deep Learning. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2021:3426080. [PMID: 35911847 PMCID: PMC9334044 DOI: 10.1155/2021/3426080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 10/13/2021] [Accepted: 10/18/2021] [Indexed: 12/24/2022]
Abstract
The incidence of glioma is increasing year by year, seriously endangering people's health. Magnetic resonance imaging (MRI) can effectively provide intracranial images of brain tumors and provide strong support for the diagnosis and treatment of the disease. Accurate segmentation of brain glioma has positive significance in medicine. However, due to the strong variability of the size, shape, and location of glioma and the large differences between different cases, the recognition and segmentation of glioma images are very difficult. Traditional methods are time-consuming, labor-intensive, and inefficient, and single-modal MRI images cannot provide comprehensive information about gliomas. Therefore, it is necessary to synthesize multimodal MRI images to identify and segment glioma MRI images. This work is based on multimodal MRI images and based on deep learning technology to achieve automatic and efficient segmentation of gliomas. The main tasks are as follows. A deep learning model based on dense blocks of holes, 3D U-Net, is proposed. It can automatically segment multimodal MRI glioma images. U-Net network is often used in image segmentation and has good performance. However, due to the strong specificity of glioma, the U-Net model cannot effectively obtain more details. Therefore, the 3D U-Net model proposed in this paper can integrate hollow convolution and densely connected blocks. In addition, this paper also combines classification loss and cross-entropy loss as the loss function of the network to improve the problem of category imbalance in glioma image segmentation tasks. The algorithm proposed in this paper has been used to perform a lot of experiments on the BraTS2018 dataset, and the results prove that this model has good segmentation performance.
Collapse
|
12
|
Sun Y, Wang C. A computation-efficient CNN system for high-quality brain tumor segmentation. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103475] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
|
13
|
Stumpo V, Kernbach JM, van Niftrik CHB, Sebök M, Fierstra J, Regli L, Serra C, Staartjes VE. Machine Learning Algorithms in Neuroimaging: An Overview. ACTA NEUROCHIRURGICA. SUPPLEMENT 2021; 134:125-138. [PMID: 34862537 DOI: 10.1007/978-3-030-85292-4_17] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Machine learning (ML) and artificial intelligence (AI) applications in the field of neuroimaging have been on the rise in recent years, and their clinical adoption is increasing worldwide. Deep learning (DL) is a field of ML that can be defined as a set of algorithms enabling a computer to be fed with raw data and progressively discover-through multiple layers of representation-more complex and abstract patterns in large data sets. The combination of ML and radiomics, namely the extraction of features from medical images, has proven valuable, too: Radiomic information can be used for enhanced image characterization and prognosis or outcome prediction. This chapter summarizes the basic concepts underlying ML application for neuroimaging and discusses technical aspects of the most promising algorithms, with a specific focus on Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), in order to provide the readership with the fundamental theoretical tools to better understand ML in neuroimaging. Applications are highlighted from a practical standpoint in the last section of the chapter, including: image reconstruction and restoration, image synthesis and super-resolution, registration, segmentation, classification, and outcome prediction.
Collapse
Affiliation(s)
- Vittorio Stumpo
- Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Julius M Kernbach
- Neurosurgical Artificial Intelligence Lab Aachen (NAILA), Department of Neurosurgery, RWTH University Hospital, Aachen, Germany
- Department of Neurosurgery, Faculty of Medicine, RWTH Aachen University, Aachen, Germany
| | - Christiaan H B van Niftrik
- Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Martina Sebök
- Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Jorn Fierstra
- Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Luca Regli
- Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Carlo Serra
- Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Victor E Staartjes
- Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
| |
Collapse
|
14
|
Biratu ES, Schwenker F, Ayano YM, Debelee TG. A Survey of Brain Tumor Segmentation and Classification Algorithms. J Imaging 2021; 7:jimaging7090179. [PMID: 34564105 PMCID: PMC8465364 DOI: 10.3390/jimaging7090179] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 08/25/2021] [Accepted: 08/28/2021] [Indexed: 01/16/2023] Open
Abstract
A brain Magnetic resonance imaging (MRI) scan of a single individual consists of several slices across the 3D anatomical view. Therefore, manual segmentation of brain tumors from magnetic resonance (MR) images is a challenging and time-consuming task. In addition, an automated brain tumor classification from an MRI scan is non-invasive so that it avoids biopsy and make the diagnosis process safer. Since the beginning of this millennia and late nineties, the effort of the research community to come-up with automatic brain tumor segmentation and classification method has been tremendous. As a result, there are ample literature on the area focusing on segmentation using region growing, traditional machine learning and deep learning methods. Similarly, a number of tasks have been performed in the area of brain tumor classification into their respective histological type, and an impressive performance results have been obtained. Considering state of-the-art methods and their performance, the purpose of this paper is to provide a comprehensive survey of three, recently proposed, major brain tumor segmentation and classification model techniques, namely, region growing, shallow machine learning and deep learning. The established works included in this survey also covers technical aspects such as the strengths and weaknesses of different approaches, pre- and post-processing techniques, feature extraction, datasets, and models' performance evaluation metrics.
Collapse
Affiliation(s)
- Erena Siyoum Biratu
- College of Electrical and Mechanical Engineering, Addis Ababa Science and Technology University, Addis Ababa 120611, Ethiopia; (E.S.B.); (T.G.D.)
| | - Friedhelm Schwenker
- Institute of Neural Information Processing, Ulm University, 89081 Ulm, Germany
- Correspondence:
| | | | - Taye Girma Debelee
- College of Electrical and Mechanical Engineering, Addis Ababa Science and Technology University, Addis Ababa 120611, Ethiopia; (E.S.B.); (T.G.D.)
- Ethiopian Artificial Intelligence Center, Addis Ababa 40782, Ethiopia;
| |
Collapse
|