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Ma Y, Chen S, Xiong H, Yao R, Zhang W, Yuan J, Duan H. LVONet: automatic classification model for large vessel occlusion based on the difference information between left and right hemispheres. Phys Med Biol 2024; 69:035012. [PMID: 38211308 DOI: 10.1088/1361-6560/ad1d6a] [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: 09/20/2023] [Accepted: 01/11/2024] [Indexed: 01/13/2024]
Abstract
Objective.Stroke is a highly lethal condition, with intracranial vessel occlusion being one of its primary causes. Intracranial vessel occlusion can typically be categorized into four types, each requiring different intervention measures. Therefore, the automatic and accurate classification of intracranial vessel occlusions holds significant clinical importance for assessing vessel occlusion conditions. However, due to the visual similarities in shape and size among different vessels and variations in the degree of vessel occlusion, the automated classification of intracranial vessel occlusions remains a challenging task. Our study proposes an automatic classification model for large vessel occlusion (LVO) based on the difference information between the left and right hemispheres.Approach.Our approach is as follows. We first introduce a dual-branch attention module to learn long-range dependencies through spatial and channel attention, guiding the model to focus on vessel-specific features. Subsequently, based on the symmetry of vessel distribution, we design a differential information classification module to dynamically learn and fuse the differential information of vessel features between the two hemispheres, enhancing the sensitivity of the classification model to occluded vessels. To optimize the feature differential information among similar vessels, we further propose a novel cooperative learning loss function to minimize changes within classes and similarities between classes.Main results.We evaluate our proposed model on an intracranial LVO data set. Compared to state-of-the-art deep learning models, our model performs optimally, achieving a classification sensitivity of 93.73%, precision of 83.33%, accuracy of 89.91% and Macro-F1 score of 87.13%.Significance.This method can adaptively focus on occluded vessel regions and effectively train in scenarios with high inter-class similarity and intra-class variability, thereby improving the performance of LVO classification.
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Affiliation(s)
- Yuqi Ma
- College of Computer and Information Science, Southwest University, Chongqing, 400715, People's Republic of China
| | - Shanxiong Chen
- College of Computer and Information Science, Southwest University, Chongqing, 400715, People's Republic of China
| | - Hailing Xiong
- College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, People's Republic of China
| | - Rui Yao
- College of Computer and Information Science, Southwest University, Chongqing, 400715, People's Republic of China
| | - Wang Zhang
- College of Computer and Information Science, Southwest University, Chongqing, 400715, People's Republic of China
| | - Jiang Yuan
- College of Computer and Information Science, Southwest University, Chongqing, 400715, People's Republic of China
| | - Haowei Duan
- College of Computer and Information Science, Southwest University, Chongqing, 400715, People's Republic of China
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Schwarz R, Bier G, Wilke V, Wilke C, Taubmann O, Ditt H, Hempel JM, Ernemann U, Horger M, Gohla G. Automated Intracranial Clot Detection: A Promising Tool for Vascular Occlusion Detection in Non-Enhanced CT. Diagnostics (Basel) 2023; 13:2863. [PMID: 37761230 PMCID: PMC10527571 DOI: 10.3390/diagnostics13182863] [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: 07/31/2023] [Revised: 08/30/2023] [Accepted: 08/30/2023] [Indexed: 09/29/2023] Open
Abstract
(1) Background: to test the diagnostic performance of a fully convolutional neural network-based software prototype for clot detection in intracranial arteries using non-enhanced computed tomography (NECT) imaging data. (2) Methods: we retrospectively identified 85 patients with stroke imaging and one intracranial vessel occlusion. An automated clot detection prototype computed clot location, clot length, and clot volume in NECT scans. Clot detection rates were compared to the visual assessment of the hyperdense artery sign by two neuroradiologists. CT angiography (CTA) was used as the ground truth. Additionally, NIHSS, ASPECTS, type of therapy, and TOAST were registered to assess the relationship between clinical parameters, image results, and chosen therapy. (3) Results: the overall detection rate of the software was 66%, while the human readers had lower rates of 46% and 24%, respectively. Clot detection rates of the automated software were best in the proximal middle cerebral artery (MCA) and the intracranial carotid artery (ICA) with 88-92% followed by the more distal MCA and basilar artery with 67-69%. There was a high correlation between greater clot length and interventional thrombectomy and between smaller clot length and rather conservative treatment. (4) Conclusions: the automated clot detection prototype has the potential to detect intracranial arterial thromboembolism in NECT images, particularly in the ICA and MCA. Thus, it could support radiologists in emergency settings to speed up the diagnosis of acute ischemic stroke, especially in settings where CTA is not available.
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Affiliation(s)
- Ricarda Schwarz
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University of Tuebingen, D-72076 Tuebingen, Germany; (R.S.); (M.H.)
| | - Georg Bier
- Department of Diagnostic and Interventional Neuroradiology, Eberhard Karls University of Tuebingen, D-72076 Tuebingen, Germany; (G.B.); (J.-M.H.); (U.E.)
- Radiologie Salzstraße, D-48143 Muenster, Germany
| | - Vera Wilke
- Department of Neurology & Stroke, Eberhard Karls University of Tuebingen, D-72076 Tuebingen, Germany;
- Centre for Neurovascular Diseases Tübingen, D-72076 Tuebingen, Germany
| | - Carlo Wilke
- Division Translational Genomics of Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, Center of Neurology, University of Tuebingen, D-72076 Tuebingen, Germany;
- German Center for Neurodegenerative Diseases (DZNE), D-72076 Tuebingen, Germany
| | - Oliver Taubmann
- Siemens Healthcare GmbH, Computed Tomography, D-91301 Forchheim, Germany; (O.T.); (H.D.)
| | - Hendrik Ditt
- Siemens Healthcare GmbH, Computed Tomography, D-91301 Forchheim, Germany; (O.T.); (H.D.)
| | - Johann-Martin Hempel
- Department of Diagnostic and Interventional Neuroradiology, Eberhard Karls University of Tuebingen, D-72076 Tuebingen, Germany; (G.B.); (J.-M.H.); (U.E.)
| | - Ulrike Ernemann
- Department of Diagnostic and Interventional Neuroradiology, Eberhard Karls University of Tuebingen, D-72076 Tuebingen, Germany; (G.B.); (J.-M.H.); (U.E.)
| | - Marius Horger
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University of Tuebingen, D-72076 Tuebingen, Germany; (R.S.); (M.H.)
| | - Georg Gohla
- Department of Diagnostic and Interventional Neuroradiology, Eberhard Karls University of Tuebingen, D-72076 Tuebingen, Germany; (G.B.); (J.-M.H.); (U.E.)
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van Voorst H, Bruggeman AAE, Yang W, Andriessen J, Welberg E, Dutra BG, Konduri PR, Arrarte Terreros N, Hoving JW, Tolhuisen ML, Kappelhof M, Brouwer J, Boodt N, van Kranendonk KR, Koopman MS, Hund HM, Krietemeijer M, van Zwam WH, van Beusekom HMM, van der Lugt A, Emmer BJ, Marquering HA, Roos YBWEM, Caan MWA, Majoie CBLM. Thrombus radiomics in patients with anterior circulation acute ischemic stroke undergoing endovascular treatment. J Neurointerv Surg 2023; 15:e79-e85. [PMID: 35882552 DOI: 10.1136/jnis-2022-019085] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Accepted: 07/10/2022] [Indexed: 11/04/2022]
Abstract
BACKGROUND Thrombus radiomics (TR) describe complex shape and textural thrombus imaging features. We aimed to study the relationship of TR extracted from non-contrast CT with procedural and functional outcome in endovascular-treated patients with acute ischemic stroke. METHODS Thrombi were segmented on thin-slice non-contrast CT (≤1 mm) from 699 patients included in the MR CLEAN Registry. In a pilot study, we selected 51 TR with consistent values across two raters' segmentations (ICC >0.75). Random forest models using TR in addition or as a substitute to baseline clinical variables (CV) and manual thrombus measurements (MTM) were trained with 499 patients and evaluated on 200 patients for predicting successful reperfusion (extended Thrombolysis in Cerebral Ischemia (eTICI) ≥2B), first attempt reperfusion, reperfusion within three attempts, and functional independence (modified Rankin Scale (mRS) ≤2). Three texture and shape features were selected based on feature importance and related to eTICI ≥2B, number of attempts to eTICI ≥2B, and 90-day mRS with ordinal logistic regression. RESULTS Random forest models using TR, CV or MTM had comparable predictive performance. Thrombus texture (inverse difference moment normalized) was independently associated with reperfusion (adjusted common OR (acOR) 0.85, 95% CI 0.72 to 0.99). Thrombus volume and texture were also independently associated with the number of attempts to successful reperfusion (acOR 1.36, 95% CI 1.03 to 1.88 and acOR 1.24, 95% CI 1.04 to 1.49). CONCLUSIONS TR describing thrombus volume and texture were associated with more attempts to successful reperfusion. Compared with models using CV and MTM, TR had no added value for predicting procedural and functional outcome.
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Affiliation(s)
- Henk van Voorst
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
- Department of Biomedical Engineering and Physics, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Agnetha A E Bruggeman
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Wenjin Yang
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
- Department of Neurosurgery, Shanghai Pudong New Area People's Hospital, Shanghai, China
| | - Jurr Andriessen
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Elise Welberg
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Bruna G Dutra
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Praneeta R Konduri
- Department of Biomedical Engineering and Physics, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Nerea Arrarte Terreros
- Department of Biomedical Engineering and Physics, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Jan W Hoving
- Department of Biomedical Engineering and Physics, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Manon L Tolhuisen
- Department of Biomedical Engineering and Physics, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Manon Kappelhof
- Department of Biomedical Engineering and Physics, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Josje Brouwer
- Department of Neurology, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Nikki Boodt
- Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Katinka R van Kranendonk
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Miou S Koopman
- Department of Biomedical Engineering and Physics, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Hajo M Hund
- Department of Radiology and Nuclear Medicine, Haaglanden Medical Center Bronovo, Den Haag, The Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Menno Krietemeijer
- Department of Radiology and Nuclear Medicine, Catharina Hospital, Eindhoven, The Netherlands
| | - Wim H van Zwam
- Department of Radiology, Maastricht University Medical Center, Maastricht, The Netherlands
| | | | - Aad van der Lugt
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Bart J Emmer
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Henk A Marquering
- Department of Biomedical Engineering and Physics, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Yvo B W E M Roos
- Department of Neurology, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Matthan W A Caan
- Department of Biomedical Engineering and Physics, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
| | - Charles B L M Majoie
- Department of Biomedical Engineering and Physics, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands
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Wagner DT, Tilmans L, Peng K, Niedermeier M, Rohl M, Ryan S, Yadav D, Takacs N, Garcia-Fraley K, Koso M, Dikici E, Prevedello LM, Nguyen XV. Artificial Intelligence in Neuroradiology: A Review of Current Topics and Competition Challenges. Diagnostics (Basel) 2023; 13:2670. [PMID: 37627929 PMCID: PMC10453240 DOI: 10.3390/diagnostics13162670] [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: 05/16/2023] [Revised: 08/07/2023] [Accepted: 08/09/2023] [Indexed: 08/27/2023] Open
Abstract
There is an expanding body of literature that describes the application of deep learning and other machine learning and artificial intelligence methods with potential relevance to neuroradiology practice. In this article, we performed a literature review to identify recent developments on the topics of artificial intelligence in neuroradiology, with particular emphasis on large datasets and large-scale algorithm assessments, such as those used in imaging AI competition challenges. Numerous applications relevant to ischemic stroke, intracranial hemorrhage, brain tumors, demyelinating disease, and neurodegenerative/neurocognitive disorders were discussed. The potential applications of these methods to spinal fractures, scoliosis grading, head and neck oncology, and vascular imaging were also reviewed. The AI applications examined perform a variety of tasks, including localization, segmentation, longitudinal monitoring, diagnostic classification, and prognostication. While research on this topic is ongoing, several applications have been cleared for clinical use and have the potential to augment the accuracy or efficiency of neuroradiologists.
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Affiliation(s)
- Daniel T. Wagner
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Luke Tilmans
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Kevin Peng
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | | | - Matt Rohl
- College of Arts and Sciences, The Ohio State University, Columbus, OH 43210, USA
| | - Sean Ryan
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Divya Yadav
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Noah Takacs
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Krystle Garcia-Fraley
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Mensur Koso
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA
| | - Engin Dikici
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Luciano M. Prevedello
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
| | - Xuan V. Nguyen
- Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA (L.M.P.)
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Dumitriu LaGrange D, Reymond P, Brina O, Zboray R, Neels A, Wanke I, Lövblad KO. Spatial heterogeneity of occlusive thrombus in acute ischemic stroke: A systematic review. J Neuroradiol 2023; 50:352-360. [PMID: 36649796 DOI: 10.1016/j.neurad.2023.01.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 01/12/2023] [Accepted: 01/12/2023] [Indexed: 01/15/2023]
Abstract
Following the advent of mechanical thrombectomy, occlusive clots in ischemic stroke have been amply characterized using conventional histopathology. Many studies have investigated the compositional variability of thrombi and the consequences of thrombus composition on treatment response. More recent evidence has emerged about the spatial heterogeneity of the clot or the preferential distribution of its components and compact nature. Here we review this emerging body of evidence, discuss its potential clinical implications, and propose the development of adequate characterization techniques.
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Affiliation(s)
- Daniela Dumitriu LaGrange
- Neurodiagnostic and Neurointerventional Division, Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Switzerland.
| | - Philippe Reymond
- Neurodiagnostic and Neurointerventional Division, Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Olivier Brina
- Division of Diagnostic and Interventional Neuroradiology, HUG Geneva University Hospitals, Geneva, Switzerland
| | - Robert Zboray
- Center for X-Ray Analytics, Swiss Federal Laboratories for Materials Science and Technology (Empa), Dübendorf 8600, Switzerland
| | - Antonia Neels
- Center for X-Ray Analytics, Swiss Federal Laboratories for Materials Science and Technology (Empa), Dübendorf 8600, Switzerland
| | - Isabel Wanke
- Division of Neuroradiology, Klinik Hirslanden, Zurich, Switzerland; Swiss Neuroradiology Institute, Zurich, Switzerland; Division of Neuroradiology, University of Essen, Essen, Germany
| | - Karl-Olof Lövblad
- Division of Diagnostic and Interventional Neuroradiology, HUG Geneva University Hospitals, Geneva, Switzerland; Neurodiagnostic and Neurointerventional Division, Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Switzerland
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Deep-Learning-Based Thrombus Localization and Segmentation in Patients with Posterior Circulation Stroke. Diagnostics (Basel) 2022; 12:diagnostics12061400. [PMID: 35741209 PMCID: PMC9222185 DOI: 10.3390/diagnostics12061400] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 05/25/2022] [Accepted: 05/29/2022] [Indexed: 12/10/2022] Open
Abstract
Thrombus volume in posterior circulation stroke (PCS) has been associated with outcome, through recanalization. Manual thrombus segmentation is impractical for large scale analysis of image characteristics. Hence, in this study we develop the first automatic method for thrombus localization and segmentation on CT in patients with PCS. In this multi-center retrospective study, 187 patients with PCS from the MR CLEAN Registry were included. We developed a convolutional neural network (CNN) that segments thrombi and restricts the volume-of-interest (VOI) to the brainstem (Polar-UNet). Furthermore, we reduced false positive localization by removing small-volume objects, referred to as volume-based removal (VBR). Polar-UNet is benchmarked against a CNN that does not restrict the VOI (BL-UNet). Performance metrics included the intra-class correlation coefficient (ICC) between automated and manually segmented thrombus volumes, the thrombus localization precision and recall, and the Dice coefficient. The majority of the thrombi were localized. Without VBR, Polar-UNet achieved a thrombus localization recall of 0.82, versus 0.78 achieved by BL-UNet. This high recall was accompanied by a low precision of 0.14 and 0.09. VBR improved precision to 0.65 and 0.56 for Polar-UNet and BL-UNet, respectively, with a small reduction in recall to 0.75 and 0.69. The Dice coefficient achieved by Polar-UNet was 0.44, versus 0.38 achieved by BL-UNet with VBR. Both methods achieved ICCs of 0.41 (95% CI: 0.27–0.54). Restricting the VOI to the brainstem improved the thrombus localization precision, recall, and segmentation overlap compared to the benchmark. VBR improved thrombus localization precision but lowered recall.
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Mittmann BJ, Braun M, Runck F, Schmitz B, Tran TN, Yamlahi A, Maier-Hein L, Franz AM. Deep learning-based classification of DSA image sequences of patients with acute ischemic stroke. Int J Comput Assist Radiol Surg 2022; 17:1633-1641. [PMID: 35604489 PMCID: PMC9463240 DOI: 10.1007/s11548-022-02654-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 04/21/2022] [Indexed: 11/05/2022]
Abstract
PURPOSE Recently, a large number of patients with acute ischemic stroke benefited from the use of thrombectomy, a minimally invasive intervention technique for mechanically removing thrombi from the cerebrovasculature. During thrombectomy, 2D digital subtraction angiography (DSA) image sequences are acquired simultaneously from the posterior-anterior and the lateral view to control whether thrombus removal was successful, and to possibly detect newly occluded areas caused by thrombus fragments split from the main thrombus. However, such new occlusions, which would be treatable by thrombectomy, may be overlooked during the intervention. To prevent this, we developed a deep learning-based approach to automatic classification of DSA sequences into thrombus-free and non-thrombus-free sequences. METHODS We performed a retrospective study based on the single-center DSA data of thrombectomy patients. For classifying the DSA sequences, we applied Long Short-Term Memory or Gated Recurrent Unit networks and combined them with different Convolutional Neural Networks used as feature extractor. These network variants were trained on the DSA data by using five-fold cross-validation. The classification performance was determined on a test data set with respect to the Matthews correlation coefficient (MCC) and the area under the curve (AUC). Finally, we evaluated our models on patient cases, in which overlooking thrombi during thrombectomy had happened. RESULTS Depending on the specific model configuration used, we obtained a performance of up to 0.77[Formula: see text]0.94 for the MCC[Formula: see text]AUC, respectively. Additionally, overlooking thrombi could have been prevented in the reported patient cases, as our models would have classified the corresponding DSA sequences correctly. CONCLUSION Our deep learning-based approach to thrombus identification in DSA sequences yielded high accuracy on our single-center test data set. External validation is now required to investigate the generalizability of our method. As demonstrated, using this new approach may help reduce the incident risk of overlooking thrombi during thrombectomy in the future.
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Affiliation(s)
- Benjamin J Mittmann
- Medical Faculty, Heidelberg University, Im Neuenheimer Feld 672, 69120, Heidelberg, BW, Germany. .,Department of Computer Science, Ulm University of Applied Sciences, Albert-Einstein-Allee 55, 89081, Ulm, BW, Germany.
| | - Michael Braun
- Neuroradiology Section, District Hospital Guenzburg, Lindenallee 2, 89312, Guenzburg, BY, Germany
| | - Frank Runck
- Neuroradiology Section, District Hospital Guenzburg, Lindenallee 2, 89312, Guenzburg, BY, Germany
| | - Bernd Schmitz
- Neuroradiology Section, District Hospital Guenzburg, Lindenallee 2, 89312, Guenzburg, BY, Germany
| | - Thuy N Tran
- Department of Computer Assisted Medical Interventions, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 223, 69120, Heidelberg, BW, Germany
| | - Amine Yamlahi
- Department of Computer Assisted Medical Interventions, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 223, 69120, Heidelberg, BW, Germany
| | - Lena Maier-Hein
- Medical Faculty, Heidelberg University, Im Neuenheimer Feld 672, 69120, Heidelberg, BW, Germany.,Department of Computer Assisted Medical Interventions, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 223, 69120, Heidelberg, BW, Germany.,Faculty of Mathematics and Computer Science, Heidelberg University, Im Neuenheimer Feld 205, 69120, Heidelberg, BW, Germany
| | - Alfred M Franz
- Department of Computer Science, Ulm University of Applied Sciences, Albert-Einstein-Allee 55, 89081, Ulm, BW, Germany. .,Department of Computer Assisted Medical Interventions, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 223, 69120, Heidelberg, BW, Germany.
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Weyland CS, Papanagiotou P, Schmitt N, Joly O, Bellot P, Mokli Y, Ringleb PA, Kastrup A, Möhlenbruch MA, Bendszus M, Nagel S, Herweh C. Hyperdense Artery Sign in Patients With Acute Ischemic Stroke-Automated Detection With Artificial Intelligence-Driven Software. Front Neurol 2022; 13:807145. [PMID: 35449516 PMCID: PMC9016329 DOI: 10.3389/fneur.2022.807145] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 02/28/2022] [Indexed: 01/22/2023] Open
Abstract
Background Hyperdense artery sign (HAS) on non-contrast CT (NCCT) can indicate a large vessel occlusion (LVO) in patients with acute ischemic stroke. HAS detection belongs to routine reporting in patients with acute stroke and can help to identify patients in whom LVO is not initially suspected. We sought to evaluate automated HAS detection by commercial software and compared its performance to that of trained physicians against a reference standard. Methods Non-contrast CT scans from 154 patients with and without LVO proven by CT angiography (CTA) were independently rated for HAS by two blinded neuroradiologists and an AI-driven algorithm (Brainomix®). Sensitivity and specificity were analyzed for the clinicians and the software. As a secondary analysis, the clot length was automatically calculated by the software and compared with the length manually outlined on CTA images as the reference standard. Results Among 154 patients, 84 (54.5%) had CTA-proven LVO. HAS on the correct side was detected with a sensitivity and specificity of 0.77 (CI:0.66–0.85) and 0.87 (0.77–0.94), 0.8 (0.69–0.88) and 0.97 (0.89–0.99), and 0.93 (0.84–0.97) and 0.71 (0.59–0.81) by the software and readers 1 and 2, respectively. The automated estimation of the thrombus length was in moderate agreement with the CTA-based reference standard [intraclass correlation coefficient (ICC) 0.73]. Conclusion Automated detection of HAS and estimation of thrombus length on NCCT by the tested software is feasible with a sensitivity and specificity comparable to that of trained neuroradiologists.
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Affiliation(s)
| | - Panagiotis Papanagiotou
- Department of Neuroradiology, Klinikum Bremen-Mitte, Bremen, Germany.,Department of Radiology, Areteion University Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - Niclas Schmitt
- Department of Neuroradiology, University of Heidelberg, Heidelberg, Germany
| | | | | | - Yahia Mokli
- Department of Neurology, University of Heidelberg, Heidelberg, Germany
| | | | - A Kastrup
- Neurology, Klinikum Bremen-Mitte, Bremen, Germany
| | | | - Martin Bendszus
- Department of Neuroradiology, University of Heidelberg, Heidelberg, Germany
| | - Simon Nagel
- Department of Neurology, University of Heidelberg, Heidelberg, Germany
| | - Christian Herweh
- Department of Neuroradiology, University of Heidelberg, Heidelberg, Germany
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9
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Fully Automated Thrombus Segmentation on CT Images of Patients with Acute Ischemic Stroke. Diagnostics (Basel) 2022; 12:diagnostics12030698. [PMID: 35328251 PMCID: PMC8947334 DOI: 10.3390/diagnostics12030698] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 02/16/2022] [Accepted: 03/10/2022] [Indexed: 11/17/2022] Open
Abstract
Thrombus imaging characteristics are associated with treatment success and functional outcomes in stroke patients. However, assessing these characteristics based on manual annotations is labor intensive and subject to observer bias. Therefore, we aimed to create an automated pipeline for consistent and fast full thrombus segmentation. We used multi-center, multi-scanner datasets of anterior circulation stroke patients with baseline NCCT and CTA for training (n = 228) and testing (n = 100). We first found the occlusion location using StrokeViewer LVO and created a bounding box around it. Subsequently, we trained dual modality U-Net based convolutional neural networks (CNNs) to segment the thrombus inside this bounding box. We experimented with: (1) U-Net with two input channels for NCCT and CTA, and U-Nets with two encoders where (2) concatenate, (3) add, and (4) weighted-sum operators were used for feature fusion. Furthermore, we proposed a dynamic bounding box algorithm to adjust the bounding box. The dynamic bounding box algorithm reduces the missed cases but does not improve Dice. The two-encoder U-Net with a weighted-sum feature fusion shows the best performance (surface Dice 0.78, Dice 0.62, and 4% missed cases). Final segmentation results have high spatial accuracies and can therefore be used to determine thrombus characteristics and potentially benefit radiologists in clinical practice.
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