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Sun J, Werdiger F, Blair C, Chen C, Yang Q, Bivard A, Lin L, Parsons M. Automatic segmentation of hemorrhagic transformation on follow-up non-contrast CT after acute ischemic stroke. Front Neuroinform 2024; 18:1382630. [PMID: 38689832 PMCID: PMC11058994 DOI: 10.3389/fninf.2024.1382630] [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: 02/06/2024] [Accepted: 03/30/2024] [Indexed: 05/02/2024] Open
Abstract
Background Hemorrhagic transformation (HT) following reperfusion therapies is a serious complication for patients with acute ischemic stroke. Segmentation and quantification of hemorrhage provides critical insights into patients' condition and aids in prognosis. This study aims to automatically segment hemorrhagic regions on follow-up non-contrast head CT (NCCT) for stroke patients treated with endovascular thrombectomy (EVT). Methods Patient data were collected from 10 stroke centers across two countries. We propose a semi-automated approach with adaptive thresholding methods, eliminating the need for extensive training data and reducing computational demands. We used Dice Similarity Coefficient (DSC) and Lin's Concordance Correlation Coefficient (Lin's CCC) to evaluate the performance of the algorithm. Results A total of 51 patients were included, with 28 Type 2 hemorrhagic infarction (HI2) cases and 23 parenchymal hematoma (PH) cases. The algorithm achieved a mean DSC of 0.66 ± 0.17. Notably, performance was superior for PH cases (mean DSC of 0.73 ± 0.14) compared to HI2 cases (mean DSC of 0.61 ± 0.18). Lin's CCC was 0.88 (95% CI 0.79-0.93), indicating a strong agreement between the algorithm's results and the ground truth. In addition, the algorithm demonstrated excellent processing time, with an average of 2.7 s for each patient case. Conclusion To our knowledge, this is the first study to perform automated segmentation of post-treatment hemorrhage for acute stroke patients and evaluate the performance based on the radiological severity of HT. This rapid and effective tool has the potential to assist with predicting prognosis in stroke patients with HT after EVT.
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Affiliation(s)
- Jiacheng Sun
- Sydney Brain Centre, The Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia
- South Western Sydney Clinical School, University of New South Wales, Sydney, NSW, Australia
| | - Freda Werdiger
- Melbourne Brain Centre at Royal Melbourne Hospital, Melbourne, VIC, Australia
- Department of Medicine, University of Melbourne, Melbourne, VIC, Australia
| | - Christopher Blair
- Sydney Brain Centre, The Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia
- South Western Sydney Clinical School, University of New South Wales, Sydney, NSW, Australia
- Department of Neurology and Neurophysiology, Liverpool Hospital, Sydney, NSW, Australia
| | - Chushuang Chen
- South Western Sydney Clinical School, University of New South Wales, Sydney, NSW, Australia
| | - Qing Yang
- Apollo Medical Imaging Technology Pty. Ltd., Melbourne, VIC, Australia
| | - Andrew Bivard
- Melbourne Brain Centre at Royal Melbourne Hospital, Melbourne, VIC, Australia
- Department of Medicine, University of Melbourne, Melbourne, VIC, Australia
| | - Longting Lin
- Sydney Brain Centre, The Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia
- South Western Sydney Clinical School, University of New South Wales, Sydney, NSW, Australia
| | - Mark Parsons
- Sydney Brain Centre, The Ingham Institute for Applied Medical Research, Liverpool, NSW, Australia
- South Western Sydney Clinical School, University of New South Wales, Sydney, NSW, Australia
- Department of Neurology and Neurophysiology, Liverpool Hospital, Sydney, NSW, Australia
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Rau A, Reisert M, Taschner CA, Demerath T, Elsheikh S, Frank B, Köhrmann M, Urbach H, Kellner E. Reducing False-Positives in CT Perfusion Infarct Core Segmentation Using Contralateral Local Normalization. AJNR Am J Neuroradiol 2024; 45:277-283. [PMID: 38302197 DOI: 10.3174/ajnr.a8111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 11/20/2023] [Indexed: 02/03/2024]
Abstract
BACKGROUND AND PURPOSE The established global threshold of rCBF <30% for infarct core segmentation can lead to false-positives, as it does not account for the differences in blood flow between GM and WM and patient-individual factors, such as microangiopathy. To mitigate this problem, we suggest normalizing each voxel not only with a global reference value (ie, the median value of normally perfused tissue) but also with its local contralateral counterpart. MATERIALS AND METHODS We retrospectively enrolled 2830 CTP scans with suspected ischemic stroke, of which 335 showed obvious signs of microangiopathy. In addition to the conventional, global normalization, a local normalization was performed by dividing the rCBF maps with their mirrored and smoothed counterpart, which sets each voxel value in relation to the contralateral counterpart, intrinsically accounting for GM and WM differences and symmetric patient individual microangiopathy. Maps were visually assessed and core volumes were calculated for both methods. RESULTS Cases with obvious microangiopathy showed a strong reduction in false-positives by using local normalization (mean 14.7 mL versus mean 3.7 mL in cases with and without microangiopathy). On average, core volumes were slightly smaller, indicating an improved segmentation that was more robust against naturally low blood flow values in the deep WM. CONCLUSIONS The proposed method of local normalization can reduce overestimation of the infarct core, especially in the deep WM and in cases with obvious microangiopathy. False-positives in CTP infarct core segmentation might lead to less-than-optimal therapy decisions when not correctly interpreted. The proposed method might help mitigate this problem.
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Affiliation(s)
- Alexander Rau
- From the Department of Neuroradiology (A.R., C.A.T., T.D., S.E., H.U.), Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Department of Diagnostic and Interventional Radiology (A.R.), Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Marco Reisert
- Medical Physics, Department of Diagnostic and Interventional Radiology (M.R., E.K.), Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Department of Stereotactic and Functional Neurosurgery (M.R.), Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Christian A Taschner
- From the Department of Neuroradiology (A.R., C.A.T., T.D., S.E., H.U.), Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Theo Demerath
- From the Department of Neuroradiology (A.R., C.A.T., T.D., S.E., H.U.), Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Samer Elsheikh
- From the Department of Neuroradiology (A.R., C.A.T., T.D., S.E., H.U.), Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Benedikt Frank
- Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (B.F., M.K.), University Hospital Essen, Essen, Germany
| | - Martin Köhrmann
- Department of Neurology and Center for Translational Neuro- and Behavioral Sciences (B.F., M.K.), University Hospital Essen, Essen, Germany
| | - Horst Urbach
- From the Department of Neuroradiology (A.R., C.A.T., T.D., S.E., H.U.), Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Elias Kellner
- Medical Physics, Department of Diagnostic and Interventional Radiology (M.R., E.K.), Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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Kumari V, Kumar N, Kumar K S, Kumar A, Skandha SS, Saxena S, Khanna NN, Laird JR, Singh N, Fouda MM, Saba L, Singh R, Suri JS. Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look. J Cardiovasc Dev Dis 2023; 10:485. [PMID: 38132653 PMCID: PMC10743870 DOI: 10.3390/jcdd10120485] [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: 07/27/2023] [Revised: 10/15/2023] [Accepted: 11/07/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND AND MOTIVATION Coronary artery disease (CAD) has the highest mortality rate; therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging solution that can image coronary arteries, but the diagnosis software via wall segmentation and quantification has been evolving. In this study, a deep learning (DL) paradigm was explored along with its bias. METHODS Using a PRISMA model, 145 best UNet-based and non-UNet-based methods for wall segmentation were selected and analyzed for their characteristics and scientific and clinical validation. This study computed the coronary wall thickness by estimating the inner and outer borders of the coronary artery IVUS cross-sectional scans. Further, the review explored the bias in the DL system for the first time when it comes to wall segmentation in IVUS scans. Three bias methods, namely (i) ranking, (ii) radial, and (iii) regional area, were applied and compared using a Venn diagram. Finally, the study presented explainable AI (XAI) paradigms in the DL framework. FINDINGS AND CONCLUSIONS UNet provides a powerful paradigm for the segmentation of coronary walls in IVUS scans due to its ability to extract automated features at different scales in encoders, reconstruct the segmented image using decoders, and embed the variants in skip connections. Most of the research was hampered by a lack of motivation for XAI and pruned AI (PAI) models. None of the UNet models met the criteria for bias-free design. For clinical assessment and settings, it is necessary to move from a paper-to-practice approach.
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Affiliation(s)
- Vandana Kumari
- School of Computer Science and Engineering, Galgotias University, Greater Noida 201310, India; (V.K.); (S.K.K.)
| | - Naresh Kumar
- Department of Applied Computational Science and Engineering, G L Bajaj Institute of Technology and Management, Greater Noida 201310, India
| | - Sampath Kumar K
- School of Computer Science and Engineering, Galgotias University, Greater Noida 201310, India; (V.K.); (S.K.K.)
| | - Ashish Kumar
- School of CSET, Bennett University, Greater Noida 201310, India;
| | - Sanagala S. Skandha
- Department of CSE, CMR College of Engineering and Technology, Hyderabad 501401, India;
| | - Sanjay Saxena
- Department of Computer Science and Engineering, IIT Bhubaneswar, Bhubaneswar 751003, India;
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India;
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA 94574, USA;
| | - Narpinder Singh
- Department of Food Science and Technology, Graphic Era, Deemed to be University, Dehradun 248002, India;
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA;
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09100 Cagliari, Italy;
| | - Rajesh Singh
- Department of Research and Innovation, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, India;
| | - Jasjit S. Suri
- Stroke Diagnostics and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA
- Department of Computer Science & Engineering, Graphic Era, Deemed to be University, Dehradun 248002, India
- Monitoring and Diagnosis Division, AtheroPoint™, Roseville, CA 95661, USA
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Ozkara BB, Karabacak M, Margetis K, Yedavalli VS, Wintermark M, Bisdas S. Assessment of Computed Tomography Perfusion Research Landscape: A Topic Modeling Study. Tomography 2023; 9:2016-2028. [PMID: 37987344 PMCID: PMC10661298 DOI: 10.3390/tomography9060158] [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: 09/22/2023] [Revised: 10/22/2023] [Accepted: 10/23/2023] [Indexed: 11/22/2023] Open
Abstract
The number of scholarly articles continues to rise. The continuous increase in scientific output poses a challenge for researchers, who must devote considerable time to collecting and analyzing these results. The topic modeling approach emerges as a novel response to this need. Considering the swift advancements in computed tomography perfusion (CTP), we deem it essential to launch an initiative focused on topic modeling. We conducted a comprehensive search of the Scopus database from 1 January 2000 to 16 August 2023, to identify relevant articles about CTP. Using the BERTopic model, we derived a group of topics along with their respective representative articles. For the 2020s, linear regression models were used to identify and interpret trending topics. From the most to the least prevalent, the topics that were identified include "Tumor Vascularity", "Stroke Assessment", "Myocardial Perfusion", "Intracerebral Hemorrhage", "Imaging Optimization", "Reperfusion Therapy", "Postprocessing", "Carotid Artery Disease", "Seizures", "Hemorrhagic Transformation", "Artificial Intelligence", and "Moyamoya Disease". The model provided insights into the trends of the current decade, highlighting "Postprocessing" and "Artificial Intelligence" as the most trending topics.
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Affiliation(s)
- Burak B. Ozkara
- Department of Neuroradiology, MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USA
| | - Mert Karabacak
- Department of Neurosurgery, Mount Sinai Health System, 1468 Madison Avenue, New York, NY 10029, USA
| | - Konstantinos Margetis
- Department of Neurosurgery, Mount Sinai Health System, 1468 Madison Avenue, New York, NY 10029, USA
| | - Vivek S. Yedavalli
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins Hospital, 600 N Wolfe Street, Baltimore, MD 21287, USA
| | - Max Wintermark
- Department of Neuroradiology, MD Anderson Cancer Center, 1400 Pressler Street, Houston, TX 77030, USA
| | - Sotirios Bisdas
- Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College London NHS Foundation Trust, London WC1N 3BG, UK
- Department of Brain Repair and Rehabilitation, Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
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