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Choi Y, Bang J, Kim SY, Seo M, Jang J. Deep learning-based multimodal segmentation of oropharyngeal squamous cell carcinoma on CT and MRI using self-configuring nnU-Net. Eur Radiol 2024; 34:5389-5400. [PMID: 38243135 DOI: 10.1007/s00330-024-10585-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 12/05/2023] [Accepted: 12/17/2023] [Indexed: 01/21/2024]
Abstract
PURPOSE To evaluate deep learning-based segmentation models for oropharyngeal squamous cell carcinoma (OPSCC) using CT and MRI with nnU-Net. METHODS This single-center retrospective study included 91 patients with OPSCC. The patients were grouped into the development (n = 56), test 1 (n = 13), and test 2 (n = 22) cohorts. In the development cohort, OPSCC was manually segmented on CT, MR, and co-registered CT-MR images, which served as the ground truth. The multimodal and multichannel input images were then trained using a self-configuring nnU-Net. For evaluation metrics, dice similarity coefficient (DSC) and mean Hausdorff distance (HD) were calculated for test cohorts. Pearson's correlation and Bland-Altman analyses were performed between ground truth and prediction volumes. Intraclass correlation coefficients (ICCs) of radiomic features were calculated for reproducibility assessment. RESULTS All models achieved robust segmentation performances with DSC of 0.64 ± 0.33 (CT), 0.67 ± 0.27 (MR), and 0.65 ± 0.29 (CT-MR) in test cohort 1 and 0.57 ± 0.31 (CT), 0.77 ± 0.08 (MR), and 0.73 ± 0.18 (CT-MR) in test cohort 2. No significant differences were found in DSC among the models. HD of CT-MR (1.57 ± 1.06 mm) and MR models (1.36 ± 0.61 mm) were significantly lower than that of the CT model (3.48 ± 5.0 mm) (p = 0.037 and p = 0.014, respectively). The correlation coefficients between the ground truth and prediction volumes for CT, MR, and CT-MR models were 0.88, 0.93, and 0.9, respectively. MR models demonstrated excellent mean ICCs of radiomic features (0.91-0.93). CONCLUSION The self-configuring nnU-Net demonstrated reliable and accurate segmentation of OPSCC on CT and MRI. The multimodal CT-MR model showed promising results for the simultaneous segmentation on CT and MRI. CLINICAL RELEVANCE STATEMENT Deep learning-based automatic detection and segmentation of oropharyngeal squamous cell carcinoma on pre-treatment CT and MRI would facilitate radiologic response assessment and radiotherapy planning. KEY POINTS • The nnU-Net framework produced a reliable and accurate segmentation of OPSCC on CT and MRI. • MR and CT-MR models showed higher DSC and lower Hausdorff distance than the CT model. • Correlation coefficients between the ground truth and predicted segmentation volumes were high in all the three models.
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Affiliation(s)
- Yangsean Choi
- Department of Radiology, Seoul St. Mary's Hospital, The Catholic University of Korea, College of Medicine, Seoul, Republic of Korea.
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Centre, 43 Olympic-Ro 88, Songpa-Gu, Seoul, 05505, Republic of Korea.
| | - Jooin Bang
- Department of Otolaryngology-Head and Neck Surgery, Seoul St. Mary's Hospital, The Catholic University of Korea, College of Medicine, Seoul, Republic of Korea
| | - Sang-Yeon Kim
- Department of Otolaryngology-Head and Neck Surgery, Seoul St. Mary's Hospital, The Catholic University of Korea, College of Medicine, Seoul, Republic of Korea
| | - Minkook Seo
- Department of Radiology, Seoul St. Mary's Hospital, The Catholic University of Korea, College of Medicine, Seoul, Republic of Korea
| | - Jinhee Jang
- Department of Radiology, Seoul St. Mary's Hospital, The Catholic University of Korea, College of Medicine, Seoul, Republic of Korea
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Tanioka S, Aydin OU, Hilbert A, Ishida F, Tsuda K, Araki T, Nakatsuka Y, Yago T, Kishimoto T, Ikezawa M, Suzuki H, Frey D. Prediction of hematoma expansion in spontaneous intracerebral hemorrhage using a multimodal neural network. Sci Rep 2024; 14:16465. [PMID: 39013990 PMCID: PMC11252350 DOI: 10.1038/s41598-024-67365-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Accepted: 07/10/2024] [Indexed: 07/18/2024] Open
Abstract
Hematoma expansion occasionally occurs in patients with intracerebral hemorrhage (ICH), associating with poor outcome. Multimodal neural networks incorporating convolutional neural network (CNN) analysis of images and neural network analysis of tabular data are known to show promising results in prediction and classification tasks. We aimed to develop a reliable multimodal neural network model that comprehensively analyzes CT images and clinical variables to predict hematoma expansion. We retrospectively enrolled ICH patients at four hospitals between 2017 and 2021, assigning patients from three hospitals to the training and validation dataset and patients from one hospital to the test dataset. Admission CT images and clinical variables were collected. CT findings were evaluated by experts. Three types of models were developed and trained: (1) a CNN model analyzing CT images, (2) a multimodal CNN model analyzing CT images and clinical variables, and (3) a non-CNN model analyzing CT findings and clinical variables with machine learning. The models were evaluated on the test dataset, focusing first on sensitivity and second on area under the receiver operating curve (AUC). Two hundred seventy-three patients (median age, 71 years [59-79]; 159 men) in the training and validation dataset and 106 patients (median age, 70 years [62-82]; 63 men) in the test dataset were included. Sensitivity and AUC of a CNN model were 1.000 (95% confidence interval [CI] 0.768-1.000) and 0.755 (95% CI 0.704-0.807); those of a multimodal CNN model were 1.000 (95% CI 0.768-1.000) and 0.799 (95% CI 0.749-0.849); and those of a non-CNN model were 0.857 (95% CI 0.572-0.982) and 0.733 (95% CI 0.625-0.840). We developed a multimodal neural network model incorporating CNN analysis of CT images and neural network analysis of clinical variables to predict hematoma expansion in ICH. The model was externally validated and showed the best performance of all the models.
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Affiliation(s)
- Satoru Tanioka
- Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Charitéplatz 1, 101117, Berlin, Germany.
- Department of Neurosurgery, Mie University Graduate School of Medicine, 2-174 Edobashi, Tsu, Mie, 5148507, Japan.
| | - Orhun Utku Aydin
- Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Charitéplatz 1, 101117, Berlin, Germany
| | - Adam Hilbert
- Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Charitéplatz 1, 101117, Berlin, Germany
| | - Fujimaro Ishida
- Department of Neurosurgery, Mie Chuo Medical Center, 2158-5 Myojin-Cho, Hisai, Tsu, Mie, 5141101, Japan
| | - Kazuhiko Tsuda
- Department of Neurosurgery, Matsusaka Chuo General Hospital, 102 Kobo, Matsusaka, Mie, 5158566, Japan
| | - Tomohiro Araki
- Department of Neurosurgery, Suzuka Kaisei Hospital, 112-1 Ko-Cho, Suzuka, Mie, 5138505, Japan
| | - Yoshinari Nakatsuka
- Department of Neurosurgery, Suzuka Kaisei Hospital, 112-1 Ko-Cho, Suzuka, Mie, 5138505, Japan
| | - Tetsushi Yago
- Department of Neurosurgery, Mie Chuo Medical Center, 2158-5 Myojin-Cho, Hisai, Tsu, Mie, 5141101, Japan
| | - Tomoyuki Kishimoto
- Department of Neurosurgery, Matsusaka Chuo General Hospital, 102 Kobo, Matsusaka, Mie, 5158566, Japan
| | - Munenari Ikezawa
- Department of Neurosurgery, Suzuka Kaisei Hospital, 112-1 Ko-Cho, Suzuka, Mie, 5138505, Japan
| | - Hidenori Suzuki
- Department of Neurosurgery, Mie University Graduate School of Medicine, 2-174 Edobashi, Tsu, Mie, 5148507, Japan
| | - Dietmar Frey
- Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Charitéplatz 1, 101117, Berlin, Germany
- Department of Neurosurgery, Charité Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany
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Hu P, Yan T, Xiao B, Shu H, Sheng Y, Wu Y, Shu L, Lv S, Ye M, Gong Y, Wu M, Zhu X. Deep learning-assisted detection and segmentation of intracranial hemorrhage in noncontrast computed tomography scans of acute stroke patients: a systematic review and meta-analysis. Int J Surg 2024; 110:3839-3847. [PMID: 38489547 PMCID: PMC11175741 DOI: 10.1097/js9.0000000000001266] [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: 12/19/2023] [Accepted: 02/21/2024] [Indexed: 03/17/2024]
Abstract
BACKGROUND Deep learning (DL)-assisted detection and segmentation of intracranial hemorrhage stroke in noncontrast computed tomography (NCCT) scans are well-established, but evidence on this topic is lacking. MATERIALS AND METHODS PubMed and Embase databases were searched from their inception to November 2023 to identify related studies. The primary outcomes included sensitivity, specificity, and the Dice Similarity Coefficient (DSC); while the secondary outcomes were positive predictive value (PPV), negative predictive value (NPV), precision, area under the receiver operating characteristic curve (AUROC), processing time, and volume of bleeding. Random-effect model and bivariate model were used to pooled independent effect size and diagnostic meta-analysis data, respectively. RESULTS A total of 36 original studies were included in this meta-analysis. Pooled results indicated that DL technologies have a comparable performance in intracranial hemorrhage detection and segmentation with high values of sensitivity (0.89, 95% CI: 0.88-0.90), specificity (0.91, 95% CI: 0.89-0.93), AUROC (0.94, 95% CI: 0.93-0.95), PPV (0.92, 95% CI: 0.91-0.93), NPV (0.94, 95% CI: 0.91-0.96), precision (0.83, 95% CI: 0.77-0.90), DSC (0.84, 95% CI: 0.82-0.87). There is no significant difference between manual labeling and DL technologies in hemorrhage quantification (MD 0.08, 95% CI: -5.45-5.60, P =0.98), but the latter takes less process time than manual labeling (WMD 2.26, 95% CI: 1.96-2.56, P =0.001). CONCLUSION This systematic review has identified a range of DL algorithms that the performance was comparable to experienced clinicians in hemorrhage lesions identification, segmentation, and quantification but with greater efficiency and reduced cost. It is highly emphasized that multicenter randomized controlled clinical trials will be needed to validate the performance of these tools in the future, paving the way for fast and efficient decision-making during clinical procedure in patients with acute hemorrhagic stroke.
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Affiliation(s)
- Ping Hu
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University
- Jiangxi Key Laboratory of Neurological Tumors and Cerebrovascular Diseases
- Jiangxi Health Commission Key Laboratory of Neurological Medicine
- Institute of Neuroscience, Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Tengfeng Yan
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University
- Jiangxi Key Laboratory of Neurological Tumors and Cerebrovascular Diseases
- Jiangxi Health Commission Key Laboratory of Neurological Medicine
- Institute of Neuroscience, Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Bing Xiao
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University
| | - Hongxin Shu
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University
- Jiangxi Key Laboratory of Neurological Tumors and Cerebrovascular Diseases
- Jiangxi Health Commission Key Laboratory of Neurological Medicine
- Institute of Neuroscience, Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Yilei Sheng
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University
- Jiangxi Key Laboratory of Neurological Tumors and Cerebrovascular Diseases
- Jiangxi Health Commission Key Laboratory of Neurological Medicine
- Institute of Neuroscience, Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Yanze Wu
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University
- Jiangxi Key Laboratory of Neurological Tumors and Cerebrovascular Diseases
- Jiangxi Health Commission Key Laboratory of Neurological Medicine
- Institute of Neuroscience, Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Lei Shu
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University
- Jiangxi Key Laboratory of Neurological Tumors and Cerebrovascular Diseases
- Jiangxi Health Commission Key Laboratory of Neurological Medicine
- Institute of Neuroscience, Nanchang University, Nanchang, Jiangxi, People’s Republic of China
| | - Shigang Lv
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University
| | - Minhua Ye
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University
| | - Yanyan Gong
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University
| | - Miaojing Wu
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University
| | - Xingen Zhu
- Department of Neurosurgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University
- Jiangxi Key Laboratory of Neurological Tumors and Cerebrovascular Diseases
- Jiangxi Health Commission Key Laboratory of Neurological Medicine
- Institute of Neuroscience, Nanchang University, Nanchang, Jiangxi, People’s Republic of China
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Elsheikh S, Elbaz A, Rau A, Demerath T, Fung C, Kellner E, Urbach H, Reisert M. Accuracy of automated segmentation and volumetry of acute intracerebral hemorrhage following minimally invasive surgery using a patch-based convolutional neural network in a small dataset. Neuroradiology 2024; 66:601-608. [PMID: 38367095 PMCID: PMC10937775 DOI: 10.1007/s00234-024-03311-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 02/08/2024] [Indexed: 02/19/2024]
Abstract
PURPOSE In cases of acute intracerebral hemorrhage (ICH) volume estimation is of prognostic and therapeutic value following minimally invasive surgery (MIS). The ABC/2 method is widely used, but suffers from inaccuracies and is time consuming. Supervised machine learning using convolutional neural networks (CNN), trained on large datasets, is suitable for segmentation tasks in medical imaging. Our objective was to develop a CNN based machine learning model for the segmentation of ICH and of the drain and volumetry of ICH following MIS of acute supratentorial ICH on a relatively small dataset. METHODS Ninety two scans were assigned to training (n = 29 scans), validation (n = 4 scans) and testing (n = 59 scans) datasets. The mean age (SD) was 70 (± 13.56) years. Male patients were 36. A hierarchical, patch-based CNN for segmentation of ICH and drain was trained. Volume of ICH was calculated from the segmentation mask. RESULTS The best performing model achieved a Dice similarity coefficient of 0.86 and 0.91 for the ICH and drain respectively. Automated ICH volumetry yielded high agreement with ground truth (Intraclass correlation coefficient = 0.94 [95% CI: 0.91, 0.97]). Average difference in the ICH volume was 1.33 mL. CONCLUSION Using a relatively small dataset, originating from different CT-scanners and with heterogeneous voxel dimensions, we applied a patch-based CNN framework and successfully developed a machine learning model, which accurately segments the intracerebral hemorrhage (ICH) and the drains. This provides automated and accurate volumetry of the bleeding in acute ICH treated with minimally invasive surgery.
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Affiliation(s)
- Samer Elsheikh
- Department of Neuroradiology, Faculty of Medicine, Medical Center - University of Freiburg, Breisacherstr. 64, 79106, Freiburg, Germany.
| | - Ahmed Elbaz
- Department of Neuroradiology, Faculty of Medicine, Medical Center - University of Freiburg, Breisacherstr. 64, 79106, Freiburg, Germany
| | - Alexander Rau
- Department of Neuroradiology, Faculty of Medicine, Medical Center - University of Freiburg, Breisacherstr. 64, 79106, Freiburg, Germany
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Medical Center - University of Freiburg, Freiburg, Germany
| | - Theo Demerath
- Department of Neuroradiology, Faculty of Medicine, Medical Center - University of Freiburg, Breisacherstr. 64, 79106, Freiburg, Germany
| | - Christian Fung
- Department of Neurosurgery, Faculty of Medicine, Medical Center - University of Freiburg, Freiburg, Germany
| | - Elias Kellner
- Medical Physics, Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Horst Urbach
- Department of Neuroradiology, Faculty of Medicine, Medical Center - University of Freiburg, Breisacherstr. 64, 79106, Freiburg, Germany
| | - Marco Reisert
- Medical Physics, Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Department of Stereotactic and Functional Neurosurgery, Faculty of Medicine, Medical Center - University of Freiburg, Freiburg, Germany
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Jiang B, Ozkara BB, Creeden S, Zhu G, Ding VY, Chen H, Lanzman B, Wolman D, Shams S, Trinh A, Li Y, Khalaf A, Parker JJ, Halpern CH, Wintermark M. Validation of a deep learning model for traumatic brain injury detection and NIRIS grading on non-contrast CT: a multi-reader study with promising results and opportunities for improvement. Neuroradiology 2023; 65:1605-1617. [PMID: 37269414 DOI: 10.1007/s00234-023-03170-5] [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: 04/03/2023] [Accepted: 05/21/2023] [Indexed: 06/05/2023]
Abstract
PURPOSE This study aimed to assess and externally validate the performance of a deep learning (DL) model for the interpretation of non-contrast computed tomography (NCCT) scans of patients with suspicion of traumatic brain injury (TBI). METHODS This retrospective and multi-reader study included patients with TBI suspicion who were transported to the emergency department and underwent NCCT scans. Eight reviewers, with varying levels of training and experience (two neuroradiology attendings, two neuroradiology fellows, two neuroradiology residents, one neurosurgery attending, and one neurosurgery resident), independently evaluated NCCT head scans. The same scans were evaluated using the version 5.0 of the DL model icobrain tbi. The establishment of the ground truth involved a thorough assessment of all accessible clinical and laboratory data, as well as follow-up imaging studies, including NCCT and magnetic resonance imaging, as a consensus amongst the study reviewers. The outcomes of interest included neuroimaging radiological interpretation system (NIRIS) scores, the presence of midline shift, mass effect, hemorrhagic lesions, hydrocephalus, and severe hydrocephalus, as well as measurements of midline shift and volumes of hemorrhagic lesions. Comparisons using weighted Cohen's kappa coefficient were made. The McNemar test was used to compare the diagnostic performance. Bland-Altman plots were used to compare measurements. RESULTS One hundred patients were included, with the DL model successfully categorizing 77 scans. The median age for the total group was 48, with the omitted group having a median age of 44.5 and the included group having a median age of 48. The DL model demonstrated moderate agreement with the ground truth, trainees, and attendings. With the DL model's assistance, trainees' agreement with the ground truth improved. The DL model showed high specificity (0.88) and positive predictive value (0.96) in classifying NIRIS scores as 0-2 or 3-4. Trainees and attendings had the highest accuracy (0.95). The DL model's performance in classifying various TBI CT imaging common data elements was comparable to that of trainees and attendings. The average difference for the DL model in quantifying the volume of hemorrhagic lesions was 6.0 mL with a wide 95% confidence interval (CI) of - 68.32 to 80.22, and for midline shift, the average difference was 1.4 mm with a 95% CI of - 3.4 to 6.2. CONCLUSION While the DL model outperformed trainees in some aspects, attendings' assessments remained superior in most instances. Using the DL model as an assistive tool benefited trainees, improving their NIRIS score agreement with the ground truth. Although the DL model showed high potential in classifying some TBI CT imaging common data elements, further refinement and optimization are necessary to enhance its clinical utility.
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Affiliation(s)
- Bin Jiang
- Department of Radiology, Neuroradiology Division, Stanford University, Stanford, CA, USA
| | | | - Sean Creeden
- Deparment of Neuroradiology, University of Illinois College of Medicine Peoria, Peoria, IL, USA
| | - Guangming Zhu
- Department of Neurology, The University of Arizona, Tucson, AZ, USA
| | - Victoria Y Ding
- Department of Medicine, Stanford University, Stanford, CA, USA
| | - Hui Chen
- Department of Neuroradiology, MD Anderson Cancer Center, Houston, TX, USA
| | - Bryan Lanzman
- Department of Radiology, Neuroradiology Division, Stanford University, Stanford, CA, USA
| | - Dylan Wolman
- Department of Neuroimaging and Neurointervention, Stanford University, Stanford, CA, USA
| | - Sara Shams
- Department of Radiology, Neuroradiology Division, Stanford University, Stanford, CA, USA
- Department of Radiology, Karolinska University Hospital, Stockholm, Sweden
- Institution for Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Austin Trinh
- Department of Neuroimaging and Neurointervention, Stanford University, Stanford, CA, USA
| | - Ying Li
- Department of Radiology, Neuroradiology Division, Stanford University, Stanford, CA, USA
| | - Alexander Khalaf
- Department of Neuroimaging and Neurointervention, Stanford University, Stanford, CA, USA
| | - Jonathon J Parker
- Device-Based Neuroelectronics Laboratory, Mayo Clinic, Phoenix, AZ, USA
- Department of Neurological Surgery, Mayo Clinic, Phoenix, AZ, USA
| | - Casey H Halpern
- Department of Neurosurgery, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
- Department of Surgery, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
| | - Max Wintermark
- Department of Neuroradiology, MD Anderson Cancer Center, Houston, TX, USA.
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MacIntosh BJ, Liu Q, Schellhorn T, Beyer MK, Groote IR, Morberg PC, Poulin JM, Selseth MN, Bakke RC, Naqvi A, Hillal A, Ullberg T, Wassélius J, Rønning OM, Selnes P, Kristoffersen ES, Emblem KE, Skogen K, Sandset EC, Bjørnerud A. Radiological features of brain hemorrhage through automated segmentation from computed tomography in stroke and traumatic brain injury. Front Neurol 2023; 14:1244672. [PMID: 37840934 PMCID: PMC10568013 DOI: 10.3389/fneur.2023.1244672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 09/05/2023] [Indexed: 10/17/2023] Open
Abstract
Introduction Radiological assessment is necessary to diagnose spontaneous intracerebral hemorrhage (ICH) and traumatic brain injury intracranial hemorrhage (TBI-bleed). Artificial intelligence (AI) deep learning tools provide a means for decision support. This study evaluates the hemorrhage segmentations produced from three-dimensional deep learning AI model that was developed using non-contrast computed tomography (CT) imaging data external to the current study. Methods Non-contrast CT imaging data from 1263 patients were accessed across seven data sources (referred to as sites) in Norway and Sweden. Patients were included based on ICH, TBI-bleed, or mild TBI diagnosis. Initial non-contrast CT images were available for all participants. Hemorrhage location frequency maps were generated. The number of estimated haematoma clusters was correlated with the total haematoma volume. Ground truth expert annotations were available for one ICH site; hence, a comparison was made with the estimated haematoma volumes. Segmentation volume estimates were used in a receiver operator characteristics (ROC) analysis for all samples (i.e., bleed detected) and then specifically for one site with few TBI-bleed cases. Results The hemorrhage frequency maps showed spatial patterns of estimated lesions consistent with ICH or TBI-bleed presentations. There was a positive correlation between the estimated number of clusters and total haematoma volume for each site (correlation range: 0.45-0.74; each p-value < 0.01) and evidence of ICH between-site differences. Relative to hand-drawn annotations for one ICH site, the VIOLA-AI segmentation mask achieved a median Dice Similarity Coefficient of 0.82 (interquartile range: 0.78 and 0.83), resulting in a small overestimate in the haematoma volume by a median of 0.47 mL (interquartile range: 0.04 and 1.75 mL). The bleed detection ROC analysis for the whole sample gave a high area-under-the-curve (AUC) of 0.92 (with sensitivity and specificity of 83.28% and 95.41%); however, when considering only the mild head injury site, the TBI-bleed detection gave an AUC of 0.70. Discussion An open-source segmentation tool was used to visualize hemorrhage locations across multiple data sources and revealed quantitative hemorrhage site differences. The automated total hemorrhage volume estimate correlated with a per-participant hemorrhage cluster count. ROC results were moderate-to-high. The VIOLA-AI tool had promising results and might be useful for various types of intracranial hemorrhage.
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Affiliation(s)
- Bradley J. MacIntosh
- Computational Radiology & Artificial Intelligence Unit, Department of Physics and Computational Radiology, Clinic for Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
- Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Hurvitz Brain Sciences, Sandra Black Centre for Brain Resilience & Recovery, Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Oslo, Norway
| | - Qinghui Liu
- Computational Radiology & Artificial Intelligence Unit, Department of Physics and Computational Radiology, Clinic for Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Till Schellhorn
- Computational Radiology & Artificial Intelligence Unit, Department of Physics and Computational Radiology, Clinic for Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Mona K. Beyer
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Inge Rasmus Groote
- Computational Radiology & Artificial Intelligence Unit, Department of Physics and Computational Radiology, Clinic for Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
- Department of Radiology, Vestfold Hospital Trust, Tønsberg, Norway
| | - Pål C. Morberg
- Computational Radiology & Artificial Intelligence Unit, Department of Physics and Computational Radiology, Clinic for Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
- Department of Radiology and Department of Surgery, Vestfold Hospital Trust, Tønsberg, Norway
| | - Joshua M. Poulin
- Hurvitz Brain Sciences, Sandra Black Centre for Brain Resilience & Recovery, Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Oslo, Norway
| | - Maiken N. Selseth
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
- Department of Diagnostic Imaging, Akershus University Hospital, Lørenskog, Norway
| | - Ragnhild C. Bakke
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Aina Naqvi
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Amir Hillal
- Department of Diagnostic Radiology, Neuroradiology, Skåne University Hospital, Lund, Sweden
| | - Teresa Ullberg
- Department of Diagnostic Radiology, Neuroradiology, Skåne University Hospital, Lund, Sweden
- Department of Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden
| | - Johan Wassélius
- Department of Diagnostic Radiology, Neuroradiology, Skåne University Hospital, Lund, Sweden
- Department of Clinical Sciences, Faculty of Medicine, Lund University, Lund, Sweden
| | - Ole M. Rønning
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Per Selnes
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
| | - Espen S. Kristoffersen
- Department of Neurology, Akershus University Hospital, Lørenskog, Norway
- Department of General Practice, Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Kyrre Eeg Emblem
- Department of Physics and Computational Radiology, Clinic for Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Karoline Skogen
- Computational Radiology & Artificial Intelligence Unit, Department of Physics and Computational Radiology, Clinic for Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Else C. Sandset
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Atle Bjørnerud
- Computational Radiology & Artificial Intelligence Unit, Department of Physics and Computational Radiology, Clinic for Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
- Department of Physics, University of Oslo, Oslo, Norway
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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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Xu M, Ouyang Y, Yuan Z. Deep Learning Aided Neuroimaging and Brain Regulation. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23114993. [PMID: 37299724 DOI: 10.3390/s23114993] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 05/15/2023] [Accepted: 05/22/2023] [Indexed: 06/12/2023]
Abstract
Currently, deep learning aided medical imaging is becoming the hot spot of AI frontier application and the future development trend of precision neuroscience. This review aimed to render comprehensive and informative insights into the recent progress of deep learning and its applications in medical imaging for brain monitoring and regulation. The article starts by providing an overview of the current methods for brain imaging, highlighting their limitations and introducing the potential benefits of using deep learning techniques to overcome these limitations. Then, we further delve into the details of deep learning, explaining the basic concepts and providing examples of how it can be used in medical imaging. One of the key strengths is its thorough discussion of the different types of deep learning models that can be used in medical imaging including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial network (GAN) assisted magnetic resonance imaging (MRI), positron emission tomography (PET)/computed tomography (CT), electroencephalography (EEG)/magnetoencephalography (MEG), optical imaging, and other imaging modalities. Overall, our review on deep learning aided medical imaging for brain monitoring and regulation provides a referrable glance for the intersection of deep learning aided neuroimaging and brain regulation.
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Affiliation(s)
- Mengze Xu
- Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Zhuhai 519087, China
- Centre for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Macau SAR 999078, China
| | - Yuanyuan Ouyang
- Nanomicro Sino-Europe Technology Company Limited, Zhuhai 519031, China
- Jiangfeng China-Portugal Technology Co., Ltd., Macau SAR 999078, China
| | - Zhen Yuan
- Centre for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Macau SAR 999078, China
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