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Luo J, Dai P, He Z, Huang Z, Liao S, Liu K. Deep learning models for ischemic stroke lesion segmentation in medical images: A survey. Comput Biol Med 2024; 175:108509. [PMID: 38677171 DOI: 10.1016/j.compbiomed.2024.108509] [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: 11/02/2023] [Revised: 02/09/2024] [Accepted: 04/21/2024] [Indexed: 04/29/2024]
Abstract
This paper provides a comprehensive review of deep learning models for ischemic stroke lesion segmentation in medical images. Ischemic stroke is a severe neurological disease and a leading cause of death and disability worldwide. Accurate segmentation of stroke lesions in medical images such as MRI and CT scans is crucial for diagnosis, treatment planning and prognosis. This paper first introduces common imaging modalities used for stroke diagnosis, discussing their capabilities in imaging lesions at different disease stages from the acute to chronic stage. It then reviews three major public benchmark datasets for evaluating stroke segmentation algorithms: ATLAS, ISLES and AISD, highlighting their key characteristics. The paper proceeds to provide an overview of foundational deep learning architectures for medical image segmentation, including CNN-based and transformer-based models. It summarizes recent innovations in adapting these architectures to the task of stroke lesion segmentation across the three datasets, analyzing their motivations, modifications and results. A survey of loss functions and data augmentations employed for this task is also included. The paper discusses various aspects related to stroke segmentation tasks, including prior knowledge, small lesions, and multimodal fusion, and then concludes by outlining promising future research directions. Overall, this comprehensive review covers critical technical developments in the field to support continued progress in automated stroke lesion segmentation.
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Affiliation(s)
- Jialin Luo
- School of Computer Science and Engineering, Central South University, Changsha, Hunan, China
| | - Peishan Dai
- School of Computer Science and Engineering, Central South University, Changsha, Hunan, China.
| | - Zhuang He
- School of Computer Science and Engineering, Central South University, Changsha, Hunan, China
| | - Zhongchao Huang
- Department of Biomedical Engineering, School of Basic Medical Science, Central South University, Changsha, Hunan, China
| | - Shenghui Liao
- School of Computer Science and Engineering, Central South University, Changsha, Hunan, China
| | - Kun Liu
- Brain Hospital of Hunan Province (The Second People's Hospital of Hunan Province), Changsha, Hunan, China
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Wu Z, Zhang X, Li F, Wang S, Li J. A feature-enhanced network for stroke lesion segmentation from brain MRI images. Comput Biol Med 2024; 174:108326. [PMID: 38599066 DOI: 10.1016/j.compbiomed.2024.108326] [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: 01/02/2024] [Revised: 03/14/2024] [Accepted: 03/15/2024] [Indexed: 04/12/2024]
Abstract
Accurate and expeditious segmentation of stroke lesions can greatly assist physicians in making accurate medical diagnoses and administering timely treatments. However, there are two limitations to the current deep learning methods. On the one hand, the attention structure utilizes only local features, which misleads the subsequent segmentation; on the other hand, simple downsampling compromises task-relevant detailed semantic information. To address these challenges, we propose a novel feature refinement and protection network (FRPNet) for stroke lesion segmentation. FRPNet employs a symmetric encoding-decoding structure and incorporates twin attention gate (TAG) and multi-dimension attention pooling (MAP) modules. The TAG module leverages the self-attention mechanism and bi-directional attention to extract both global and local features of the lesion. On the other hand, the MAP module establishes multidimensional pooling attention to effectively mitigate the loss of features during the encoding process. Extensive comparative experiments show that, our method significantly outperforms the state-of-the-art approaches with 60.16% DSC, 36.20px HD and 85.72% DSC, 27.02px HD on two ischemic stroke datasets that contain all stroke stages and several sequences of stroke images. The excellent results that exceed those of existing methods illustrate the efficacy and generalizability of the proposed method. The source code is released on https://github.com/wu2ze2lin2/FRPNet.
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Affiliation(s)
- Zelin Wu
- College of Electronic Information and Optical Engineering, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Xueying Zhang
- College of Electronic Information and Optical Engineering, Taiyuan University of Technology, Taiyuan, 030024, China.
| | - Fenglian Li
- College of Electronic Information and Optical Engineering, Taiyuan University of Technology, Taiyuan, 030024, China.
| | - Suzhe Wang
- College of Electronic Information and Optical Engineering, Taiyuan University of Technology, Taiyuan, 030024, China
| | - Jiaying Li
- The first clinical medical College, Shanxi Medical University, Taiyuan, 030024, China
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3
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Soltanpour M, Boulanger P, Buck B. CT Perfusion Map Synthesis from CTP Dynamic Images Using a Learned LSTM Generative Adversarial Network for Acute Ischemic Stroke Assessment. J Med Syst 2024; 48:37. [PMID: 38564061 DOI: 10.1007/s10916-024-02054-2] [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: 06/04/2023] [Accepted: 03/17/2024] [Indexed: 04/04/2024]
Abstract
Computed tomography perfusion (CTP) is a dynamic 4-dimensional imaging technique (3-dimensional volumes captured over approximately 1 min) in which cerebral blood flow is quantified by tracking the passage of a bolus of intravenous contrast with serial imaging of the brain. To diagnose and assess acute ischemic stroke, the standard method relies on summarizing acquired CTPs over the time axis to create maps that show different hemodynamic parameters, such as the timing of the bolus arrival and passage (Tmax and MTT), cerebral blood flow (CBF), and cerebral blood volume (CBV). However, producing accurate CTP maps requires the selection of an arterial input function (AIF), i.e. a time-concentration curve in one of the large feeding arteries of the brain, which is a highly error-prone procedure. Moreover, during approximately one minute of CT scanning, the brain is exposed to ionizing radiation that can alter tissue composition, and create free radicals that increase the risk of cancer. This paper proposes a novel end-to-end deep neural network that synthesizes CTP images to generate CTP maps using a learned LSTM Generative Adversarial Network (LSTM-GAN). Our proposed method can improve the precision and generalizability of CTP map extraction by eliminating the error-prone and expert-dependent AIF selection step. Further, our LSTM-GAN does not require the entire CTP time series and can produce CTP maps with a reduced number of time points. By reducing the scanning sequence from about 40 to 9 time points, the proposed method has the potential to minimize scanning time thereby reducing patient exposure to CT radiation. Our evaluations using the ISLES 2018 challenge dataset consisting of 63 patients showed that our model can generate CTP maps by using only 9 snapshots, without AIF selection, with an accuracy of 84.37 % .
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Affiliation(s)
- Mohsen Soltanpour
- Computing Science Department, University of Alberta, Edmonton, Canada.
| | - Pierre Boulanger
- Computing Science Department, University of Alberta, Edmonton, Canada
| | - Brian Buck
- Medicine Deptment, University of Alberta, Edmonton, Canada
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4
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Wu M, Yu K, Zhao Z, Zhu B. Knowledge structure and global trends of machine learning in stroke over the past decade: A scientometric analysis. Heliyon 2024; 10:e24230. [PMID: 38288018 PMCID: PMC10823080 DOI: 10.1016/j.heliyon.2024.e24230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 11/23/2023] [Accepted: 01/04/2024] [Indexed: 01/31/2024] Open
Abstract
Objective Machine learning (ML) models have been widely applied in stroke prediction, diagnosis, treatment, and prognosis assessment. We aimed to conduct a comprehensive scientometrics analysis of studies related to ML in stroke and reveal its current status, knowledge structure, and global trends. Methods All documents related to ML in stroke were retrieved from the Web of Science database on March 15, 2023. We refined the documents by including only original articles and reviews in the English language. The literature published over the past decade was imported into scientometrics software for influence detection and collaborative network analysis. Results 2389 related publications were included. The annual publication outputs demonstrated explosive growth, with an average growth rate of 63.99 %. Among the 90 countries/regions involved, the United States (729 articles) and China (636 articles) were the most productive countries. Frontiers in Neurology was the most prolific journal with 94 articles. 234 highly cited articles, each with more than 31 citations, were detected. Keyword analysis revealed a total of 5333 keywords, with a predominant focus on the application of ML models in the early diagnosis, classification, and prediction of "acute ischemic stroke" and "atrial fibrillation-related stroke". The keyword "classification" had the first and longest burst, spanning from 2013 to 2018. 'Upport vector machine' got the strongest burst strength with 6.2. Keywords such as 'mechanical thrombectomy', 'expression', and 'prognosis' experienced bursts in 2022 and have continued to be prominent. Conclusion The applications of ML in stroke are increasingly diverse and extensive, with researchers showing growing interest over the past decade. However, the clinical application of ML in stroke is still in its early stages, and several limitations and challenges need to be addressed for its widespread adoption in clinical practice.
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Affiliation(s)
- Mingfen Wu
- Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Kefu Yu
- Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Zhigang Zhao
- Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Bin Zhu
- Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
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Malik M, Chong B, Fernandez J, Shim V, Kasabov NK, Wang A. Stroke Lesion Segmentation and Deep Learning: A Comprehensive Review. Bioengineering (Basel) 2024; 11:86. [PMID: 38247963 PMCID: PMC10813717 DOI: 10.3390/bioengineering11010086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 01/05/2024] [Accepted: 01/15/2024] [Indexed: 01/23/2024] Open
Abstract
Stroke is a medical condition that affects around 15 million people annually. Patients and their families can face severe financial and emotional challenges as it can cause motor, speech, cognitive, and emotional impairments. Stroke lesion segmentation identifies the stroke lesion visually while providing useful anatomical information. Though different computer-aided software are available for manual segmentation, state-of-the-art deep learning makes the job much easier. This review paper explores the different deep-learning-based lesion segmentation models and the impact of different pre-processing techniques on their performance. It aims to provide a comprehensive overview of the state-of-the-art models and aims to guide future research and contribute to the development of more robust and effective stroke lesion segmentation models.
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Affiliation(s)
- Mishaim Malik
- Auckland Bioengineering Institute, The University of Auckland, Auckland 1010, New Zealand; (M.M.); (B.C.); (N.K.K.)
| | - Benjamin Chong
- Auckland Bioengineering Institute, The University of Auckland, Auckland 1010, New Zealand; (M.M.); (B.C.); (N.K.K.)
- Faculty of Medical and Health Sciences, The University of Auckland, Auckland 1010, New Zealand
- Centre for Brain Research, The University of Auckland, Auckland 1010, New Zealand
| | - Justin Fernandez
- Auckland Bioengineering Institute, The University of Auckland, Auckland 1010, New Zealand; (M.M.); (B.C.); (N.K.K.)
- Centre for Brain Research, The University of Auckland, Auckland 1010, New Zealand
- Mātai Medical Research Institute, Gisborne 4010, New Zealand
| | - Vickie Shim
- Auckland Bioengineering Institute, The University of Auckland, Auckland 1010, New Zealand; (M.M.); (B.C.); (N.K.K.)
- Mātai Medical Research Institute, Gisborne 4010, New Zealand
| | - Nikola Kirilov Kasabov
- Auckland Bioengineering Institute, The University of Auckland, Auckland 1010, New Zealand; (M.M.); (B.C.); (N.K.K.)
- Knowledge Engineering and Discovery Research Innovation, School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand
- Institute for Information and Communication Technologies, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria
- Knowledge Engineering Consulting Ltd., Auckland 1071, New Zealand
| | - Alan Wang
- Auckland Bioengineering Institute, The University of Auckland, Auckland 1010, New Zealand; (M.M.); (B.C.); (N.K.K.)
- Faculty of Medical and Health Sciences, The University of Auckland, Auckland 1010, New Zealand
- Centre for Brain Research, The University of Auckland, Auckland 1010, New Zealand
- Mātai Medical Research Institute, Gisborne 4010, New Zealand
- Medical Imaging Research Centre, The University of Auckland, Auckland 1010, New Zealand
- Centre for Co-Created Ageing Research, The University of Auckland, Auckland 1010, New Zealand
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6
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Gilotra K, Swarna S, Mani R, Basem J, Dashti R. Role of artificial intelligence and machine learning in the diagnosis of cerebrovascular disease. Front Hum Neurosci 2023; 17:1254417. [PMID: 37746051 PMCID: PMC10516608 DOI: 10.3389/fnhum.2023.1254417] [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: 07/07/2023] [Accepted: 08/23/2023] [Indexed: 09/26/2023] Open
Abstract
Introduction Cerebrovascular diseases are known to cause significant morbidity and mortality to the general population. In patients with cerebrovascular disease, prompt clinical evaluation and radiographic interpretation are both essential in optimizing clinical management and in triaging patients for critical and potentially life-saving neurosurgical interventions. With recent advancements in the domains of artificial intelligence (AI) and machine learning (ML), many AI and ML algorithms have been developed to further optimize the diagnosis and subsequent management of cerebrovascular disease. Despite such advances, further studies are needed to substantively evaluate both the diagnostic accuracy and feasibility of these techniques for their application in clinical practice. This review aims to analyze the current use of AI and MI algorithms in the diagnosis of, and clinical decision making for cerebrovascular disease, and to discuss both the feasibility and future applications of utilizing such algorithms. Methods We review the use of AI and ML algorithms to assist clinicians in the diagnosis and management of ischemic stroke, hemorrhagic stroke, intracranial aneurysms, and arteriovenous malformations (AVMs). After identifying the most widely used algorithms, we provide a detailed analysis of the accuracy and effectiveness of these algorithms in practice. Results The incorporation of AI and ML algorithms for cerebrovascular patients has demonstrated improvements in time to detection of intracranial pathologies such as intracerebral hemorrhage (ICH) and infarcts. For ischemic and hemorrhagic strokes, commercial AI software platforms such as RapidAI and Viz.AI have bene implemented into routine clinical practice at many stroke centers to expedite the detection of infarcts and ICH, respectively. Such algorithms and neural networks have also been analyzed for use in prognostication for such cerebrovascular pathologies. These include predicting outcomes for ischemic stroke patients, hematoma expansion, risk of aneurysm rupture, bleeding of AVMs, and in predicting outcomes following interventions such as risk of occlusion for various endovascular devices. Preliminary analyses have yielded promising sensitivities when AI and ML are used in concert with imaging modalities and a multidisciplinary team of health care providers. Conclusion The implementation of AI and ML algorithms to supplement clinical practice has conferred a high degree of accuracy, efficiency, and expedited detection in the clinical and radiographic evaluation and management of ischemic and hemorrhagic strokes, AVMs, and aneurysms. Such algorithms have been explored for further purposes of prognostication for these conditions, with promising preliminary results. Further studies should evaluate the longitudinal implementation of such techniques into hospital networks and residency programs to supplement clinical practice, and the extent to which these techniques improve patient care and clinical outcomes in the long-term.
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Affiliation(s)
| | | | | | | | - Reza Dashti
- Dashti Lab, Department of Neurological Surgery, Stony Brook University Hospital, Stony Brook, NY, United States
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7
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Liu L, Chang J, Liu Z, Zhang P, Xu X, Shang H. Hybrid Contextual Semantic Network for Accurate Segmentation and Detection of Small-Size Stroke Lesions From MRI. IEEE J Biomed Health Inform 2023; 27:4062-4073. [PMID: 37155390 DOI: 10.1109/jbhi.2023.3273771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Stroke is a cerebrovascular disease with high mortality and disability rates. The occurrence of the stroke typically produces lesions of different sizes, with the accurate segmentation and detection of small-size stroke lesions being closely related to the prognosis of patients. However, the large lesions are usually correctly identified, the small-size lesions are usually ignored. This article provides a hybrid contextual semantic network (HCSNet) that can accurately and simultaneously segment and detect small-size stroke lesions from magnetic resonance images. HCSNet inherits the advantages of the encoder-decoder architecture and applies a novel hybrid contextual semantic module that generates high-quality contextual semantic features from the spatial and channel contextual semantic features through the skip connection layer. Moreover, a mixing-loss function is proposed to optimize HCSNet for unbalanced small-size lesions. HCSNet is trained and evaluated on 2D magnetic resonance images produced from the Anatomical Tracings of Lesions After Stroke challenge (ATLAS R2.0). Extensive experiments demonstrate that HCSNet outperforms several other state-of-the-art methods in its ability to segment and detect small-size stroke lesions. Visualization and ablation experiments reveal that the hybrid semantic module improves the segmentation and detection performance of HCSNet.
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Wu J, Guo D, Wang L, Yang S, Zheng Y, Shapey J, Vercauteren T, Bisdas S, Bradford R, Saeed S, Kitchen N, Ourselin S, Zhang S, Wang G. TISS-net: Brain tumor image synthesis and segmentation using cascaded dual-task networks and error-prediction consistency. Neurocomputing 2023; 544:None. [PMID: 37528990 PMCID: PMC10243514 DOI: 10.1016/j.neucom.2023.126295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 03/15/2023] [Accepted: 04/30/2023] [Indexed: 08/03/2023]
Abstract
Accurate segmentation of brain tumors from medical images is important for diagnosis and treatment planning, and it often requires multi-modal or contrast-enhanced images. However, in practice some modalities of a patient may be absent. Synthesizing the missing modality has a potential for filling this gap and achieving high segmentation performance. Existing methods often treat the synthesis and segmentation tasks separately or consider them jointly but without effective regularization of the complex joint model, leading to limited performance. We propose a novel brain Tumor Image Synthesis and Segmentation network (TISS-Net) that obtains the synthesized target modality and segmentation of brain tumors end-to-end with high performance. First, we propose a dual-task-regularized generator that simultaneously obtains a synthesized target modality and a coarse segmentation, which leverages a tumor-aware synthesis loss with perceptibility regularization to minimize the high-level semantic domain gap between synthesized and real target modalities. Based on the synthesized image and the coarse segmentation, we further propose a dual-task segmentor that predicts a refined segmentation and error in the coarse segmentation simultaneously, where a consistency between these two predictions is introduced for regularization. Our TISS-Net was validated with two applications: synthesizing FLAIR images for whole glioma segmentation, and synthesizing contrast-enhanced T1 images for Vestibular Schwannoma segmentation. Experimental results showed that our TISS-Net largely improved the segmentation accuracy compared with direct segmentation from the available modalities, and it outperformed state-of-the-art image synthesis-based segmentation methods.
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Affiliation(s)
- Jianghao Wu
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Dong Guo
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Lu Wang
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Shuojue Yang
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuanjie Zheng
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Jonathan Shapey
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
| | - Tom Vercauteren
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Sotirios Bisdas
- Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London, UK
| | - Robert Bradford
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
| | - Shakeel Saeed
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
| | - Neil Kitchen
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
| | - Sebastien Ourselin
- School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
| | - Shaoting Zhang
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
- SenseTime Research, Shanghai, China
| | - Guotai Wang
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
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Iporre-Rivas A, Saur D, Rohr K, Scheuermann G, Gillmann C. Stroke-GFCN: ischemic stroke lesion prediction with a fully convolutional graph network. J Med Imaging (Bellingham) 2023; 10:044502. [PMID: 37465592 PMCID: PMC10350625 DOI: 10.1117/1.jmi.10.4.044502] [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: 06/14/2022] [Revised: 06/13/2023] [Accepted: 06/20/2023] [Indexed: 07/20/2023] Open
Abstract
Purpose The interpretation of image data plays a critical role during acute brain stroke diagnosis, and promptly defining the requirement of a surgical intervention will drastically impact the patient's outcome. However, determining stroke lesions purely from images can be a daunting task. Many studies proposed automatic segmentation methods for brain stroke lesions from medical images in different modalities, though heretofore results do not satisfy the requirements to be clinically reliable. We investigate the segmentation of brain stroke lesions using a geometric deep learning model that takes advantage of the intrinsic interconnected diffusion features in a set of multi-modal inputs consisting of computer tomography (CT) perfusion parameters. Approach We propose a geometric deep learning model for the segmentation of ischemic stroke brain lesions that employs spline convolutions and unpooling/pooling operators on graphs to excerpt graph-structured features in a fully convolutional network architecture. In addition, we seek to understand the underlying principles governing the different components of our model. Accordingly, we structure the experiments in two parts: an evaluation of different architecture hyperparameters and a comparison with state-of-the-art methods. Results The ablation study shows that deeper layers obtain a higher Dice coefficient score (DCS) of up to 0.3654. Comparing different pooling and unpooling methods shows that the best performing unpooling method is the proportional approach, yet it often smooths the segmentation border. Unpooling achieves segmentation results more adapted to the lesion boundary corroborated with systematic lower values of Hausdorff distance. The model performs at the level of state-of-the-art models without optimized training methods, such as augmentation or patches, with a DCS of 0.4553 ± 0.0031 . Conclusions We proposed and evaluated an end-to-end trainable fully convolutional graph network architecture using spline convolutional layers for the ischemic stroke lesion prediction. We propose a model that employs graph-based operations to predict acute stroke brain lesions from CT perfusion parameters. Our results prove the feasibility of using geometric deep learning to solve segmentation problems, and our model shows a better performance than other models evaluated. The proposed model achieves improved metric values for the DCS metric, ranging from 8.61% to 69.05%, compared with other models trained under the same conditions. Next, we compare different pooling and unpooling operations in relation to their segmentation results, and we show that the model can produce segmentation outputs that adapt to irregular segmentation boundaries when using simple heuristic unpooling operations.
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Affiliation(s)
- Ariel Iporre-Rivas
- Leipzig University, Institute for Computer Science, Faculty of Mathematics and Computer Science, Signal and Image Processing Group, Leipzig, Germany
- Max-Plank-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- ScaDS.AI, Leipzig, Germany
| | - Dorothee Saur
- Leipzig University, Department of Neurology, Leipzig, Germany
| | - Karl Rohr
- Heidelberg University, BioQuant Center, IPMB and DKFZ, Biomedical Computer Vision Group, Heidelberg, Germany
| | - Gerik Scheuermann
- Leipzig University, Institute for Computer Science, Faculty of Mathematics and Computer Science, Signal and Image Processing Group, Leipzig, Germany
| | - Christina Gillmann
- Leipzig University, Institute for Computer Science, Faculty of Mathematics and Computer Science, Signal and Image Processing Group, Leipzig, Germany
- ScaDS.AI, Leipzig, Germany
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10
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Gao Y, Dai Y, Liu F, Chen W, Shi L. An anatomy-aware framework for automatic segmentation of parotid tumor from multimodal MRI. Comput Biol Med 2023; 161:107000. [PMID: 37201442 DOI: 10.1016/j.compbiomed.2023.107000] [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: 10/04/2022] [Revised: 03/10/2023] [Accepted: 05/02/2023] [Indexed: 05/20/2023]
Abstract
Magnetic Resonance Imaging (MRI) plays an important role in diagnosing the parotid tumor, where accurate segmentation of tumors is highly desired for determining appropriate treatment plans and avoiding unnecessary surgery. However, the task remains nontrivial and challenging due to ambiguous boundaries and various sizes of the tumor, as well as the presence of a large number of anatomical structures around the parotid gland that are similar to the tumor. To overcome these problems, we propose a novel anatomy-aware framework for automatic segmentation of parotid tumors from multimodal MRI. First, a Transformer-based multimodal fusion network PT-Net is proposed in this paper. The encoder of PT-Net extracts and fuses contextual information from three modalities of MRI from coarse to fine, to obtain cross-modality and multi-scale tumor information. The decoder stacks the feature maps of different modalities and calibrates the multimodal information using the channel attention mechanism. Second, considering that the segmentation model is prone to be disturbed by similar anatomical structures and make wrong predictions, we design anatomy-aware loss. By calculating the distance between the activation regions of the prediction segmentation and the ground truth, our loss function forces the model to distinguish similar anatomical structures with the tumor and make correct predictions. Extensive experiments with MRI scans of the parotid tumor showed that our PT-Net achieved higher segmentation accuracy than existing networks. The anatomy-aware loss outperformed state-of-the-art loss functions for parotid tumor segmentation. Our framework can potentially improve the quality of preoperative diagnosis and surgery planning of parotid tumors.
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Affiliation(s)
- Yifan Gao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China
| | - Yin Dai
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China; Engineering Center on Medical Imaging and Intelligent Analysis, Ministry Education, Northeastern University, Shenyang, 110169, China.
| | - Fayu Liu
- Department of Oromaxillofacial-Head and Neck Surgery, School of Stomatology, China Medical University, Shenyang, 110002, China
| | - Weibing Chen
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, China; Engineering Center on Medical Imaging and Intelligent Analysis, Ministry Education, Northeastern University, Shenyang, 110169, China
| | - Lifu Shi
- Liaoning Jiayin Medical Technology Co., LTD, Shenyang, 110170, China
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Kn BP, Cs A, Mohammed A, Chitta KK, To XV, Srour H, Nasrallah F. An end-end deep learning framework for lesion segmentation on multi-contrast MR images-an exploratory study in a rat model of traumatic brain injury. Med Biol Eng Comput 2023; 61:847-865. [PMID: 36624356 DOI: 10.1007/s11517-022-02752-4] [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: 02/07/2022] [Accepted: 12/24/2022] [Indexed: 01/11/2023]
Abstract
Traumatic brain injury (TBI) engenders traumatic necrosis and penumbra-areas of secondary neural injury which are crucial targets for therapeutic interventions. Segmenting manually areas of ongoing changes like necrosis, edema, hematoma, and inflammation is tedious, error-prone, and biased. Using the multi-parametric MR data from a rodent model study, we demonstrate the effectiveness of an end-end deep learning global-attention-based UNet (GA-UNet) framework for automatic segmentation and quantification of TBI lesions. Longitudinal MR scans (2 h, 1, 3, 7, 14, 30, and 60 days) were performed on eight Sprague-Dawley rats after controlled cortical injury was performed. TBI lesion and sub-regions segmentation was performed using 3D-UNet and GA-UNet. Dice statistics (DSI) and Hausdorff distance were calculated to assess the performance. MR scan variations-based (bias, noise, blur, ghosting) data augmentation was performed to develop a robust model.Training/validation median DSI for U-Net was 0.9368 with T2w and MPRAGE inputs, whereas GA-UNet had 0.9537 for the same. Testing accuracies were higher for GA-UNet than U-Net with a DSI of 0.8232 for the T2w-MPRAGE inputs.Longitudinally, necrosis remained constant while oligemia and penumbra decreased, and edema appearing around day 3 which increased with time. GA-UNet shows promise for multi-contrast MR image-based segmentation/quantification of TBI in large cohort studies.
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Affiliation(s)
- Bhanu Prakash Kn
- Clinical Data Analytics & Radiomics, Cellular Image Informatics, Bioinformatics Institute, A*STAR, 30 Biopolis St Matrix, Singapore, 138671, Singapore. .,Cellular Image Informatics, Bioinformatics Institute, A*STAR Horizontal Technology Centers, Singapore, Singapore.
| | - Arvind Cs
- Clinical Data Analytics & Radiomics, Cellular Image Informatics, Bioinformatics Institute, A*STAR, 30 Biopolis St Matrix, Singapore, 138671, Singapore
| | - Abdalla Mohammed
- Queensland Brain Institute, The University of Queensland, Building 79, Upland Road, Saint Lucia, Brisbane, QLD, 4072, Australia
| | - Krishna Kanth Chitta
- Signal and Image Processing Group, Laboratory of Molecular Imaging, Singapore Bioimaging Consortium, A*STAR, 02-02 Helios 11, Biopolis Way, Singapore, 138667, Singapore
| | - Xuan Vinh To
- Queensland Brain Institute, The University of Queensland, Building 79, Upland Road, Saint Lucia, Brisbane, QLD, 4072, Australia
| | - Hussein Srour
- Queensland Brain Institute, The University of Queensland, Building 79, Upland Road, Saint Lucia, Brisbane, QLD, 4072, Australia
| | - Fatima Nasrallah
- Queensland Brain Institute, The University of Queensland, Building 79, Upland Road, Saint Lucia, Brisbane, QLD, 4072, Australia
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12
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Zhao Y, Wang X, Che T, Bao G, Li S. Multi-task deep learning for medical image computing and analysis: A review. Comput Biol Med 2023; 153:106496. [PMID: 36634599 DOI: 10.1016/j.compbiomed.2022.106496] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 12/06/2022] [Accepted: 12/27/2022] [Indexed: 12/29/2022]
Abstract
The renaissance of deep learning has provided promising solutions to various tasks. While conventional deep learning models are constructed for a single specific task, multi-task deep learning (MTDL) that is capable to simultaneously accomplish at least two tasks has attracted research attention. MTDL is a joint learning paradigm that harnesses the inherent correlation of multiple related tasks to achieve reciprocal benefits in improving performance, enhancing generalizability, and reducing the overall computational cost. This review focuses on the advanced applications of MTDL for medical image computing and analysis. We first summarize four popular MTDL network architectures (i.e., cascaded, parallel, interacted, and hybrid). Then, we review the representative MTDL-based networks for eight application areas, including the brain, eye, chest, cardiac, abdomen, musculoskeletal, pathology, and other human body regions. While MTDL-based medical image processing has been flourishing and demonstrating outstanding performance in many tasks, in the meanwhile, there are performance gaps in some tasks, and accordingly we perceive the open challenges and the perspective trends. For instance, in the 2018 Ischemic Stroke Lesion Segmentation challenge, the reported top dice score of 0.51 and top recall of 0.55 achieved by the cascaded MTDL model indicate further research efforts in high demand to escalate the performance of current models.
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Affiliation(s)
- Yan Zhao
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
| | - Xiuying Wang
- School of Computer Science, The University of Sydney, Sydney, NSW, 2008, Australia.
| | - Tongtong Che
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
| | - Guoqing Bao
- School of Computer Science, The University of Sydney, Sydney, NSW, 2008, Australia
| | - Shuyu Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
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13
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Chaki J, Woźniak M. Deep learning for neurodegenerative disorder (2016 to 2022): A systematic review. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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14
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Kaothanthong N, Atsavasirilert K, Sarampakhul S, Chantangphol P, Songsaeng D, Makhanov S. Artificial intelligence for localization of the acute ischemic stroke by non-contrast computed tomography. PLoS One 2022; 17:e0277573. [PMID: 36454916 PMCID: PMC9714826 DOI: 10.1371/journal.pone.0277573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Accepted: 10/29/2022] [Indexed: 12/03/2022] Open
Abstract
A non-contrast cranial computer tomography (ncCT) is often employed for the diagnosis of the early stage of the ischemic stroke. However, the number of false negatives is high. More accurate results are obtained by an MRI. However, the MRI is not available in every hospital. Moreover, even if it is available in the clinic for the routine tests, emergency often does not have it. Therefore, this paper proposes an end-to-end framework for detection and segmentation of the brain infarct on the ncCT. The computer tomography perfusion (CTp) is used as the ground truth. The proposed ensemble model employs three deep convolution neural networks (CNNs) to process three end-to-end feature maps and a hand-craft features characterized by specific contra-lateral features. To improve the accuracy of the detected infarct area, the spatial dependencies between neighboring slices are employed at the postprocessing step. The numerical experiments have been performed on 18 ncCT-CTp paired stroke cases (804 image-pairs). The leave-one-out approach is applied for evaluating the proposed method. The model achieves 91.16% accuracy, 65.15% precision, 77.44% recall, 69.97% F1 score, and 0.4536 IoU.
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Affiliation(s)
- Natsuda Kaothanthong
- Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, Thailand
- * E-mail: (NK); (SM)
| | - Kamin Atsavasirilert
- Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, Thailand
| | - Soawapot Sarampakhul
- Division of Diagnostic Radiology, Department of Radiology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Pantid Chantangphol
- Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, Thailand
| | - Dittapong Songsaeng
- Division of Diagnostic Radiology, Department of Radiology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Stanislav Makhanov
- Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, Thailand
- * E-mail: (NK); (SM)
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15
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Kumar A, Ghosal P, Kundu SS, Mukherjee A, Nandi D. A lightweight asymmetric U-Net framework for acute ischemic stroke lesion segmentation in CT and CTP images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107157. [PMID: 36208537 DOI: 10.1016/j.cmpb.2022.107157] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 09/02/2022] [Accepted: 09/24/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVES This paper has introduced a patch-based, residual, asymmetric, encoder-decoder CNN that solves two major problems in acute ischemic stroke lesion segmentation from CT and CT perfusion data using deep neural networks. First, the class imbalance is encountered since the lesion core size covers less than 5% of the volume of the entire brain. Second, deeper neural networks face the drawback of vanishing gradients, and this degrades the learning ability of the network. METHODS The neural network architecture has been designed for better convergence and faster inference time without compromising performance to address these difficulties. It uses a training strategy combining Focal Tversky and Binary cross-entropy loss functions to overcome the class imbalance issue. The model comprises only four resolution steps with a total of 11 convolutional layers. A base filter of 8, used for the residual connection with two convolutional blocks at the encoder side, is doubled after each resolution step. Simultaneously, the decoder consists of residual blocks with one convolutional layer and a constant number of 8 filters in each resolution step. This proposition allows for a lighter build with fewer trainable parameters as well as aids in avoiding overfitting by allowing the decoder to decode only necessary information. RESULTS The presented method has been evaluated through submission on the publicly accessible platform of the Ischemic Stroke Lesion Segmentation (ISLES) 2018 medical image segmentation challenge achieving the second-highest testing dice similarity coefficient (DSC). The experimental results demonstrate that the proposed model achieves comparable performance to other submitted strategies in terms of DSC Precision, Recall, and Absolute Volume Difference (AVD). CONCLUSIONS Through the proposed approach, the two major research gaps are coherently addressed while achieving high challenge scores by solving the mentioned problems. Our model can serve as a tool for clinicians and radiologists to hasten decision-making and detect strokes efficiently.
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Affiliation(s)
- Amish Kumar
- Department of Computer Science and Engineering, NIT Durgapur, 713209, India.
| | - Palash Ghosal
- Department of Information Technology, Sikkim Manipal Institute of Technology, 737136, India.
| | - Soumya Snigdha Kundu
- Department of Computer Science and Engineering, SRM Institute of Science and Technology, Kattankulathur, 603203, India.
| | - Amritendu Mukherjee
- Department of Interventional Radiology, Rashid Hospital, Dubai, 4545, United Arab Emirates.
| | - Debashis Nandi
- Department of Computer Science and Engineering, NIT Durgapur, 713209, India.
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16
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Diagnostic Value of Image Features of Magnetic Resonance Imaging in Intracranial Hemorrhage and Cerebral Infarction. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:6495568. [PMID: 35935302 PMCID: PMC9296345 DOI: 10.1155/2022/6495568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 06/09/2022] [Accepted: 06/13/2022] [Indexed: 11/17/2022]
Abstract
This study aimed to investigate the differential diagnosis value of routine magnetic resonance imaging (MRI) and magnetic resonance diffusion-weighted imaging (DWI) in hyperacute intracranial hemorrhage (HICH) and hyperacute cerebral infarction (HCI). Fifty-five patients with HICH were set as group A, and 55 patients with HCI were selected as group B. All the patients underwent routine MRI and DWI examinations. The morphological distribution and signal characteristics (low, high, or mixed) of the lesions in the two groups were recorded. The diagnostic accuracy, sensitivity, and specificity of routine MRI and DWI were compared for distinguishing HICH and HCI. The results suggested that the lesions in patients with HICH were mainly manifested as mixed signals (40 cases), while those in patients with HCI showed high signals (48 cases). HICH occurred in the basal ganglia in 44 cases, in the brain stem in 6 cases, in the cerebellum in 4 cases, in the cerebral cortex in 0 cases, and in the corpus callosum in 1 case. HCI occurred in the basal ganglia area, brain stem, cerebellum, cerebral cortex, and corpus callosum in 5, 3, 35, 12, and 0 cases, respectively. The diagnostic accuracy, specificity, and sensitivity of DWI for HICH and HCI were significantly higher than those of routine MRI (P < 0.05). It was indicated that compared with routine MRI, DWI was more effective in the diagnosis of HICH and HCI, with clearer and more accurate images and better diagnostic performance.
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17
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Wang X, Fan Y, Zhang N, Li J, Duan Y, Yang B. Performance of Machine Learning for Tissue Outcome Prediction in Acute Ischemic Stroke: A Systematic Review and Meta-Analysis. Front Neurol 2022; 13:910259. [PMID: 35873778 PMCID: PMC9305175 DOI: 10.3389/fneur.2022.910259] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 06/20/2022] [Indexed: 12/03/2022] Open
Abstract
Machine learning (ML) has been proposed for lesion segmentation in acute ischemic stroke (AIS). This study aimed to provide a systematic review and meta-analysis of the overall performance of current ML algorithms for final infarct prediction from baseline imaging. We made a comprehensive literature search on eligible studies developing ML models for core infarcted tissue estimation on admission CT or MRI in AIS patients. Eleven studies meeting the inclusion criteria were included in the quantitative analysis. Study characteristics, model methodology, and predictive performance of the included studies were extracted. A meta-analysis was conducted on the dice similarity coefficient (DSC) score by using a random-effects model to assess the overall predictive performance. Study heterogeneity was assessed by Cochrane Q and Higgins I2 tests. The pooled DSC score of the included ML models was 0.50 (95% CI 0.39–0.61), with high heterogeneity observed across studies (I2 96.5%, p < 0.001). Sensitivity analyses using the one-study removed method showed the adjusted overall DSC score ranged from 0.47 to 0.52. Subgroup analyses indicated that the DL-based models outperformed the conventional ML classifiers with the best performance observed in DL algorithms combined with CT data. Despite the presence of heterogeneity, current ML-based approaches for final infarct prediction showed moderate but promising performance. Before well integrated into clinical stroke workflow, future investigations are suggested to train ML models on large-scale, multi-vendor data, validate on external cohorts and adopt formalized reporting standards for improving model accuracy and robustness.
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Affiliation(s)
- Xinrui Wang
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China
| | - Yiming Fan
- Department of Orthopedics, Chinese PLA General Hospital, Beijing, China
| | - Nan Zhang
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China
| | - Jing Li
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Yang Duan
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China
| | - Benqiang Yang
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China
- *Correspondence: Benqiang Yang
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18
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Iqbal A, Sharif M, Yasmin M, Raza M, Aftab S. Generative adversarial networks and its applications in the biomedical image segmentation: a comprehensive survey. INTERNATIONAL JOURNAL OF MULTIMEDIA INFORMATION RETRIEVAL 2022; 11:333-368. [PMID: 35821891 PMCID: PMC9264294 DOI: 10.1007/s13735-022-00240-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 03/16/2022] [Accepted: 05/24/2022] [Indexed: 05/13/2023]
Abstract
Recent advancements with deep generative models have proven significant potential in the task of image synthesis, detection, segmentation, and classification. Segmenting the medical images is considered a primary challenge in the biomedical imaging field. There have been various GANs-based models proposed in the literature to resolve medical segmentation challenges. Our research outcome has identified 151 papers; after the twofold screening, 138 papers are selected for the final survey. A comprehensive survey is conducted on GANs network application to medical image segmentation, primarily focused on various GANs-based models, performance metrics, loss function, datasets, augmentation methods, paper implementation, and source codes. Secondly, this paper provides a detailed overview of GANs network application in different human diseases segmentation. We conclude our research with critical discussion, limitations of GANs, and suggestions for future directions. We hope this survey is beneficial and increases awareness of GANs network implementations for biomedical image segmentation tasks.
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Affiliation(s)
- Ahmed Iqbal
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan
| | - Muhammad Sharif
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan
| | - Mussarat Yasmin
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan
| | - Mudassar Raza
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan
| | - Shabib Aftab
- Department of Computer Science, Virtual University of Pakistan, Lahore, Pakistan
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19
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Samak ZA, Clatworthy P, Mirmehdi M. FeMA: Feature matching auto-encoder for predicting ischaemic stroke evolution and treatment outcome. Comput Med Imaging Graph 2022; 99:102089. [PMID: 35738186 DOI: 10.1016/j.compmedimag.2022.102089] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 05/04/2022] [Accepted: 06/03/2022] [Indexed: 01/05/2023]
Abstract
Although, predicting ischaemic stroke evolution and treatment outcome provide important information one step towards individual treatment planning, classifying functional outcome and modelling the brain tissue evolution remains a challenge due to data complexity and visually subtle changes in the brain. We propose a novel deep learning approach, Feature Matching Auto-encoder (FeMA) that consists of two stages, predicting ischaemic stroke evolution at one week without voxel-wise annotation and predicting ischaemic stroke treatment outcome at 90 days from a baseline scan. In the first stage, we introduce feature similarity and consistency objective, and in the second stage, we show that adding stroke evolution information increase the performance of functional outcome prediction. Comparative experiments demonstrate that our proposed method is more effective to extract representative follow-up features and achieves the best results for functional outcome of stroke treatment.
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Affiliation(s)
- Zeynel A Samak
- Department of Computer Science, University of Bristol, Bristol, UK.
| | - Philip Clatworthy
- Translational Health Sciences, University of Bristol, Bristol, UK; Stroke Neurology, Southmead Hospital, North Bristol NHS Trust, Bristol, UK.
| | - Majid Mirmehdi
- Department of Computer Science, University of Bristol, Bristol, UK.
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20
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Sreejith S, Subramanian R, Karthik S. Using patching asymmetric regions to assess ischemic stroke lesion in neuro imaging. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-212457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Ischemic stroke is a universal ailment that endangers the life of patients and makes them bedridden until death. Over a decade, doctors and radiologists have been dissecting patient status straightforwardly from the printouts of the slice images delivered by different diagnostic imaging modalities. Computed Tomography (CT) is a frequently used imaging strategy for therapeutic analysis and neuroanatomical investigations. The main objective of the paper is to develop a simple technique with less architectural complication and power consumption. The proposed work is to section the ischemic stroke lesion more efficiently from multi-succession CT images using patching the asymmetric region. The Hough transform segment and extracts the features from the asymmetric region of the CT image and finally, the random forest is implemented to classify the unusual tissues from the CT image dependent on their pathological properties. RF classifier has been trained for different parts of the cerebrum for fragmenting the stroke lesion. The acquired outcomes produce better segmentation accuracy when compared with different strategies. The overall efficiency of the proposed method determines the Ischemic stroke with an accuracy of 95% with an RF classifier. Hence this method can be used in the segmentation process of stroke lesions.
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Affiliation(s)
- S. Sreejith
- Department of Electronics & Communication Engineering, SNS College of Technology, Coimbatore, Tamilnadu, India
| | - R. Subramanian
- Department of Electrical & Electronics Engineering, SNS College of Technology, Coimbatore, Tamilnadu, India
| | - S. Karthik
- Department of Computer Science & Engineering, SNS College of Technology, Coimbatore, Tamilnadu, India
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21
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Lin SY, Chiang PL, Chen PW, Cheng LH, Chen MH, Chang PC, Lin WC, Chen YS. Toward automated segmentation for acute ischemic stroke using non-contrast computed tomography. Int J Comput Assist Radiol Surg 2022; 17:661-671. [PMID: 35257285 DOI: 10.1007/s11548-022-02570-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 01/26/2022] [Indexed: 11/05/2022]
Abstract
PURPOSE Non-contrast computed tomography (NCCT) is a first-line imaging technique for determining treatment options for acute ischemic stroke (AIS). However, its poor contrast and signal-to-noise ratio limit the diagnosis accuracy for radiologists, and automated AIS lesion segmentation using NCCT also remains a challenge. In this paper, we propose R2U-RNet, a novel model for AIS lesion segmentation using NCCT. METHODS We used an in-house retrospective NCCT dataset with 261 AIS patients with manual lesion segmentation using follow-up diffusion-weighted images. R2U-RNet is based on an R2U-Net backbone with a novel residual refinement unit. Each input image contains two image channels from separate preprocessing procedures. The proposed model incorporates multiscale focal loss to mitigate the class imbalance problem and to leverage the importance of different levels of details. A proposed noisy-label training scheme is utilized to account for uncertainties in the manual annotations. RESULTS The proposed model outperformed several iconic segmentation models in AIS lesion segmentation using NCCT, and our ablation study demonstrated the efficacy of the proposed model. Statistical analysis of segmentation performance revealed significant effects of regional stroke occurrence and side of the stroke, suggesting the importance of region-specific information for automated segmentation, and the potential influence of the hemispheric difference in clinical data. CONCLUSION This study demonstrated the potentials of R2U-RNet model for automated NCCT AIS lesion segmentation. The proposed model can serve as a tool for accelerating AIS diagnoses and improving the treatment quality of AIS patients.
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Affiliation(s)
- Shih-Yen Lin
- Department of Computer Science, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu, Taiwan
| | - Pi-Ling Chiang
- Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, 123 Ta-Pei Road, Niao-Sung, Kaohsiung, 83305, Taiwan
| | - Peng-Wen Chen
- Institute of Biomedical Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Li-Hsin Cheng
- Department of Computer Science, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu, Taiwan.,Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
| | - Meng-Hsiang Chen
- Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, 123 Ta-Pei Road, Niao-Sung, Kaohsiung, 83305, Taiwan
| | - Pei-Chun Chang
- Department of Computer Science, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu, Taiwan
| | - Wei-Che Lin
- Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, 123 Ta-Pei Road, Niao-Sung, Kaohsiung, 83305, Taiwan.
| | - Yong-Sheng Chen
- Department of Computer Science, National Yang Ming Chiao Tung University, 1001 University Road, Hsinchu, Taiwan.
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22
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Diao S, Tian Y, Hu W, Hou J, Lambo R, Zhang Z, Xie Y, Nie X, Zhang F, Racoceanu D, Qin W. Weakly Supervised Framework for Cancer Region Detection of Hepatocellular Carcinoma in Whole-Slide Pathologic Images Based on Multiscale Attention Convolutional Neural Network. THE AMERICAN JOURNAL OF PATHOLOGY 2022; 192:553-563. [PMID: 34896390 DOI: 10.1016/j.ajpath.2021.11.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 11/10/2021] [Accepted: 11/17/2021] [Indexed: 06/14/2023]
Abstract
Visual inspection of hepatocellular carcinoma cancer regions by experienced pathologists in whole-slide images (WSIs) is a challenging, labor-intensive, and time-consuming task because of the large scale and high resolution of WSIs. Therefore, a weakly supervised framework based on a multiscale attention convolutional neural network (MSAN-CNN) was introduced into this process. Herein, patch-based images with image-level normal/tumor annotation (rather than images with pixel-level annotation) were fed into a classification neural network. To further improve the performances of cancer region detection, multiscale attention was introduced into the classification neural network. A total of 100 cases were obtained from The Cancer Genome Atlas and divided into 70 training and 30 testing data sets that were fed into the MSAN-CNN framework. The experimental results showed that this framework significantly outperforms the single-scale detection method according to the area under the curve and accuracy, sensitivity, and specificity metrics. When compared with the diagnoses made by three pathologists, MSAN-CNN performed better than a junior- and an intermediate-level pathologist, and slightly worse than a senior pathologist. Furthermore, MSAN-CNN provided a very fast detection time compared with the pathologists. Therefore, a weakly supervised framework based on MSAN-CNN has great potential to assist pathologists in the fast and accurate detection of cancer regions of hepatocellular carcinoma on WSIs.
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Affiliation(s)
- Songhui Diao
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Shenzhen College of Advanced Technology, University of Chinese Academy of Science, Shenzhen, China
| | - Yinli Tian
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China
| | - Wanming Hu
- Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jiaxin Hou
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Shenzhen College of Advanced Technology, University of Chinese Academy of Science, Shenzhen, China
| | - Ricardo Lambo
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zhicheng Zhang
- Department of Radiation Oncology, Stanford University, Stanford, California
| | - Yaoqin Xie
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Shenzhen College of Advanced Technology, University of Chinese Academy of Science, Shenzhen, China
| | - Xiu Nie
- Department of Pathology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Fa Zhang
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Daniel Racoceanu
- Sorbonne Université, Paris Brain Institute-Institut du Cerveau-ICM, Institut National de Santé et en Recherche Médicale, Centre National de Recherche Scientifique, Assistance Publique Hôpitaux de Paris, Hôpital de la Pitié-Salpêtrière, Paris, France
| | - Wenjian Qin
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Shenzhen College of Advanced Technology, University of Chinese Academy of Science, Shenzhen, China.
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23
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Shin H, Agyeman R, Rafiq M, Chang MC, Choi GS. Automated segmentation of chronic stroke lesion using efficient U-Net architecture. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.01.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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24
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Inamdar MA, Raghavendra U, Gudigar A, Chakole Y, Hegde A, Menon GR, Barua P, Palmer EE, Cheong KH, Chan WY, Ciaccio EJ, Acharya UR. A Review on Computer Aided Diagnosis of Acute Brain Stroke. SENSORS (BASEL, SWITZERLAND) 2021; 21:8507. [PMID: 34960599 PMCID: PMC8707263 DOI: 10.3390/s21248507] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 12/05/2021] [Accepted: 12/09/2021] [Indexed: 01/01/2023]
Abstract
Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., 'ischemic penumbra') can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke management. Abiding to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, we have surveyed a total of 177 research papers published between 2010 and 2021 to highlight the current status and challenges faced by computer aided diagnosis (CAD), machine learning (ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation. This work concludes by showcasing the current requirement of this domain, the preferred modality, and prospective research areas.
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Affiliation(s)
- Mahesh Anil Inamdar
- Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India;
| | - Udupi Raghavendra
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (Y.C.)
| | - Anjan Gudigar
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (Y.C.)
| | - Yashas Chakole
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (Y.C.)
| | - Ajay Hegde
- Department of Neurosurgery, Kasturba Medical College, Manipal Academy of Higher Education, Manipal 576104, India; (A.H.); (G.R.M.)
| | - Girish R. Menon
- Department of Neurosurgery, Kasturba Medical College, Manipal Academy of Higher Education, Manipal 576104, India; (A.H.); (G.R.M.)
| | - Prabal Barua
- School of Management & Enterprise, University of Southern Queensland, Toowoomba, QLD 4350, Australia;
- Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW 2007, Australia
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia
| | - Elizabeth Emma Palmer
- School of Women’s and Children’s Health, University of New South Wales, Sydney, NSW 2052, Australia;
| | - Kang Hao Cheong
- Science, Mathematics and Technology Cluster, Singapore University of Technology and Design, Singapore 487372, Singapore;
| | - Wai Yee Chan
- Department of Biomedical Imaging, Research Imaging Centre, University of Malaya, Kuala Lumpur 59100, Malaysia;
| | - Edward J. Ciaccio
- Department of Medicine, Columbia University, New York, NY 10032, USA;
| | - U. Rajendra Acharya
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia;
- School of Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
- Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore 599491, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
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SCOAT-Net: A novel network for segmenting COVID-19 lung opacification from CT images. PATTERN RECOGNITION 2021; 119:108109. [PMID: 34127870 PMCID: PMC8189738 DOI: 10.1016/j.patcog.2021.108109] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 05/07/2021] [Accepted: 06/09/2021] [Indexed: 02/05/2023]
Abstract
Automatic segmentation of lung opacification from computed tomography (CT) images shows excellent potential for quickly and accurately quantifying the infection of Coronavirus disease 2019 (COVID-19) and judging the disease development and treatment response. However, some challenges still exist, including the complexity and variability features of the opacity regions, the small difference between the infected and healthy tissues, and the noise of CT images. Due to limited medical resources, it is impractical to obtain a large amount of data in a short time, which further hinders the training of deep learning models. To answer these challenges, we proposed a novel spatial- and channel-wise coarse-to-fine attention network (SCOAT-Net), inspired by the biological vision mechanism, for the segmentation of COVID-19 lung opacification from CT images. With the UNet++ as basic structure, our SCOAT-Net introduces the specially designed spatial-wise and channel-wise attention modules, which serve to collaboratively boost the attention learning of the network and extract the efficient features of the infected opacification regions at the pixel and channel levels. Experiments show that our proposed SCOAT-Net achieves better results compared to several state-of-the-art image segmentation networks and has acceptable generalization ability.
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Wu J, Zhang S, Li X, Chen J, Xu H, Zheng J, Gao Y, Tian Y, Liang Y, Ji R. Joint segmentation and detection of COVID-19 via a sequential region generation network. PATTERN RECOGNITION 2021; 118:108006. [PMID: 34002101 PMCID: PMC8116317 DOI: 10.1016/j.patcog.2021.108006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Revised: 04/14/2021] [Accepted: 04/27/2021] [Indexed: 05/03/2023]
Abstract
The fast pandemics of coronavirus disease (COVID-19) has led to a devastating influence on global public health. In order to treat the disease, medical imaging emerges as a useful tool for diagnosis. However, the computed tomography (CT) diagnosis of COVID-19 requires experts' extensive clinical experience. Therefore, it is essential to achieve rapid and accurate segmentation and detection of COVID-19. This paper proposes a simple yet efficient and general-purpose network, called Sequential Region Generation Network (SRGNet), to jointly detect and segment the lesion areas of COVID-19. SRGNet can make full use of the supervised segmentation information and then outputs multi-scale segmentation predictions. Through this, high-quality lesion-areas suggestions can be generated on the predicted segmentation maps, reducing the diagnosis cost. Simultaneously, the detection results conversely refine the segmentation map by a post-processing procedure, which significantly improves the segmentation accuracy. The superiorities of our SRGNet over the state-of-the-art methods are validated through extensive experiments on the built COVID-19 database.
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Affiliation(s)
- Jipeng Wu
- Media Analytics and Computing Lab, Department of Artificial Intelligence, School of Informatics, Xiamen University, 361005, China
- National Institute for Data Science in Health and Medicine, Xiamen University, 361005, China
- Peng Cheng Laboratory, 518055, China
| | - Shengchuan Zhang
- Media Analytics and Computing Lab, Department of Artificial Intelligence, School of Informatics, Xiamen University, 361005, China
- National Institute for Data Science in Health and Medicine, Xiamen University, 361005, China
| | - Xi Li
- Peking University Shenzhen Hospital, 518038, China
| | - Jie Chen
- Peng Cheng Laboratory, 518055, China
- School of Electronic and Computer Engineering, Peking University, 518055, China
| | - Haibo Xu
- Department of Radiology, Zhongnan hospital of Wuhan university, 430064, China
| | - Jiawen Zheng
- Media Analytics and Computing Lab, Department of Artificial Intelligence, School of Informatics, Xiamen University, 361005, China
| | - Yue Gao
- School of Software, Tsinghua University, 100084, China
| | | | | | - Rongrong Ji
- Media Analytics and Computing Lab, Department of Artificial Intelligence, School of Informatics, Xiamen University, 361005, China
- National Institute for Data Science in Health and Medicine, Xiamen University, 361005, China
- Peng Cheng Laboratory, 518055, China
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27
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Soltanpour M, Greiner R, Boulanger P, Buck B. Improvement of automatic ischemic stroke lesion segmentation in CT perfusion maps using a learned deep neural network. Comput Biol Med 2021; 137:104849. [PMID: 34530336 DOI: 10.1016/j.compbiomed.2021.104849] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 08/11/2021] [Accepted: 09/04/2021] [Indexed: 10/20/2022]
Abstract
Acute ischemic stroke is one of the leading causes of death and long-term disability worldwide. It occurs when a blood clot blocks an artery that supplies blood to the brain tissue. Segmentation of acute ischemic stroke lesions plays a vital role to improve diagnosis, outcome assessment, and treatment planning. The current standard approach of ischemic stroke lesion segmentation is simply thresholding the Computed Tomography Perfusion (CTP) maps, i.e., quantitative feature maps created by summarizing CTP time sequence scans. However, this approach is not precise enough (its Dice similarity score is only around 50%) to be used in practice. Numerous machine learning-based techniques have recently been proposed to improve the accuracy of ischemic stroke lesion segmentation. Although they have achieved remarkable results, they still need to be improved before they can be used in actual practice. This paper presents a novel deep learning-based technique, MutiRes U-Net, for the segmentation of ischemic stroke lesions in CTP maps. MultiRes U-Net is a modified version of the original U-Net that is re-designed to be robust to segment the objects in different scales and unusual appearances. Additionally, in this paper, we propose to enrich the input CTP maps by using their contra-lateral and corresponding Tmax images. We evaluated the proposed method using the ISLES challenge 2018 dataset. As compared to the state-of-the-art methods, the results show an improvement in segmentation task accuracy. The dice similarity score (DSC) was 68%, the Jaccard score was 57.13%, and the mean absolute volume error was 22.62(ml).
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Affiliation(s)
| | - Russ Greiner
- Department of Computing Science, University of Alberta, Canada.
| | | | - Brian Buck
- Department of Medicine, University of Alberta, Canada.
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28
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de la Rosa E, Sima DM, Menze B, Kirschke JS, Robben D. AIFNet: Automatic vascular function estimation for perfusion analysis using deep learning. Med Image Anal 2021; 74:102211. [PMID: 34425318 DOI: 10.1016/j.media.2021.102211] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 06/25/2021] [Accepted: 08/04/2021] [Indexed: 12/30/2022]
Abstract
Perfusion imaging is crucial in acute ischemic stroke for quantifying the salvageable penumbra and irreversibly damaged core lesions. As such, it helps clinicians to decide on the optimal reperfusion treatment. In perfusion CT imaging, deconvolution methods are used to obtain clinically interpretable perfusion parameters that allow identifying brain tissue abnormalities. Deconvolution methods require the selection of two reference vascular functions as inputs to the model: the arterial input function (AIF) and the venous output function, with the AIF as the most critical model input. When manually performed, the vascular function selection is time demanding, suffers from poor reproducibility and is subject to the professionals' experience. This leads to potentially unreliable quantification of the penumbra and core lesions and, hence, might harm the treatment decision process. In this work we automatize the perfusion analysis with AIFNet, a fully automatic and end-to-end trainable deep learning approach for estimating the vascular functions. Unlike previous methods using clustering or segmentation techniques to select vascular voxels, AIFNet is directly optimized at the vascular function estimation, which allows to better recognise the time-curve profiles. Validation on the public ISLES18 stroke database shows that AIFNet almost reaches inter-rater performance for the vascular function estimation and, subsequently, for the parameter maps and core lesion quantification obtained through deconvolution. We conclude that AIFNet has potential for clinical transfer and could be incorporated in perfusion deconvolution software.
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Affiliation(s)
- Ezequiel de la Rosa
- icometrix, Leuven, Belgium; Department of Computer Science, Technical University of Munich, Munich, Germany.
| | | | - Bjoern Menze
- Department of Computer Science, Technical University of Munich, Munich, Germany
| | - Jan S Kirschke
- Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany
| | - David Robben
- icometrix, Leuven, Belgium; Medical Imaging Research Center (MIRC), KU Leuven, Leuven, Belgium; Medical Image Computing (MIC), ESAT-PSI, Department of Electrical Engineering, KU Leuven, Leuven, Belgium
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MCA-DN: Multi-path convolution leveraged attention deep network for salvageable tissue detection in ischemic stroke from multi-parametric MRI. Comput Biol Med 2021; 136:104724. [PMID: 34388469 DOI: 10.1016/j.compbiomed.2021.104724] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 07/16/2021] [Accepted: 07/30/2021] [Indexed: 11/24/2022]
Abstract
BACKGROUND AND OBJECTIVE Accurate and timely treatment of ischemic stroke can restore the blood flow in the affected area and reduce the risk of disability and death. Identification and localisation of both direct and collateral blood flow restriction from MRI using computational intelligence play a crucial role in assisting manual diagnosis decisions in stroke treatment. METHOD A novel multi-path convolution leveraged attention based deep network (MCA-DN) is proposed to address this challenge. MCA-DN combines multi-path convolution derived attention making different weighted filters in each attention convolution sub-path, with interactions on the same level of abstraction. This facilitates the network to focus on voxels with enhanced weighted activations, directing to a plausible lesion. Such a proposition of acquiring attention by embedding multiple filter paths, also prioritizes the selective activation of multi-parametric MRI sequences. The multi-path convolution assisted attention block allows the network layers to gain more insights on the input tensor, enabling the expansion of hypothesis search space with a controlled parameter count. RESULTS The algorithm is evaluated on 139 patients of 3 datasets with 4 sub-datasets, including 2 benchmarked challenge datasets of ISLES-2015, 2017. MCA-DN achieved parametric measures of Dice similarity coefficient: 77.3 %, sensitivity: 82.8 %, and specificity: 98.8 %, for stroke segmentation, outperforming the five state-of-the-art methods in the field with encouraging success. CONCLUSION Competitive performance of the MCA-DN demonstrates immense potential to assist patient-specific stroke treatment planning by estimating the benefit of reperfusion.
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30
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Meta grayscale adaptive network for 3D integrated renal structures segmentation. Med Image Anal 2021; 71:102055. [PMID: 33866259 DOI: 10.1016/j.media.2021.102055] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 02/03/2021] [Accepted: 03/26/2021] [Indexed: 11/22/2022]
Abstract
Three-dimensional (3D) integrated renal structures (IRS) segmentation targets segmenting the kidneys, renal tumors, arteries, and veins in one inference. Clinicians will benefit from the 3D IRS visual model for accurate preoperative planning and intraoperative guidance of laparoscopic partial nephrectomy (LPN). However, no success has been reported in 3D IRS segmentation due to the inherent challenges in grayscale distribution: low contrast caused by the narrow task-dependent distribution range of regions of interest (ROIs), and the networks representation preferences caused by the distribution variation inter-images. In this paper, we propose the Meta Greyscale Adaptive Network (MGANet), the first deep learning framework to simultaneously segment the kidney, renal tumors, arteries and veins on CTA images in one inference. It makes innovations in two collaborate aspects: 1) The Grayscale Interest Search (GIS) adaptively focuses segmentation networks on task-dependent grayscale distributions via scaling the window width and center with two cross-correlated coefficients for the first time, thus learning the fine-grained representation for fine segmentation. 2) The Meta Grayscale Adaptive (MGA) learning makes an image-level meta-learning strategy. It represents diverse robust features from multiple distributions, perceives the distribution characteristic, and generates the model parameters to fuse features dynamically according to image's distribution, thus adapting the grayscale distribution variation. This study enrolls 123 patients and the average Dice coefficients of the renal structures are up to 87.9%. Fine selection of the task-dependent grayscale distribution ranges and personalized fusion of multiple representations on different distributions will lead to better 3D IRS segmentation quality. Extensive experiments with promising results on renal structures reveal powerful segmentation accuracy and great clinical significance in renal cancer treatment.
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