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Kuanar S, Cai J, Nakai H, Nagayama H, Takahashi H, LeGout J, Kawashima A, Froemming A, Mynderse L, Dora C, Humphreys M, Klug J, Korfiatis P, Erickson B, Takahashi N. Transition-zone PSA-density calculated from MRI deep learning prostate zonal segmentation model for prediction of clinically significant prostate cancer. Abdom Radiol (NY) 2024; 49:3722-3734. [PMID: 38896250 DOI: 10.1007/s00261-024-04301-z] [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: 01/15/2024] [Revised: 03/19/2024] [Accepted: 03/20/2024] [Indexed: 06/21/2024]
Abstract
PURPOSE To develop a deep learning (DL) zonal segmentation model of prostate MR from T2-weighted images and evaluate TZ-PSAD for prediction of the presence of csPCa (Gleason score of 7 or higher) compared to PSAD. METHODS 1020 patients with a prostate MRI were randomly selected to develop a DL zonal segmentation model. Test dataset included 20 cases in which 2 radiologists manually segmented both the peripheral zone (PZ) and TZ. Pair-wise Dice index was calculated for each zone. For the prediction of csPCa using PSAD and TZ-PSAD, we used 3461 consecutive MRI exams performed in patients without a history of prostate cancer, with pathological confirmation and available PSA values, but not used in the development of the segmentation model as internal test set and 1460 MRI exams from PI-CAI challenge as external test set. PSAD and TZ-PSAD were calculated from the segmentation model output. The area under the receiver operating curve (AUC) was compared between PSAD and TZ-PSAD using univariate and multivariate analysis (adjusts age) with the DeLong test. RESULTS Dice scores of the model against two radiologists were 0.87/0.87 and 0.74/0.72 for TZ and PZ, while those between the two radiologists were 0.88 for TZ and 0.75 for PZ. For the prediction of csPCa, the AUCs of TZPSAD were significantly higher than those of PSAD in both internal test set (univariate analysis, 0.75 vs. 0.73, p < 0.001; multivariate analysis, 0.80 vs. 0.78, p < 0.001) and external test set (univariate analysis, 0.76 vs. 0.74, p < 0.001; multivariate analysis, 0.77 vs. 0.75, p < 0.001 in external test set). CONCLUSION DL model-derived zonal segmentation facilitates the practical measurement of TZ-PSAD and shows it to be a slightly better predictor of csPCa compared to the conventional PSAD. Use of TZ-PSAD may increase the sensitivity of detecting csPCa by 2-5% for a commonly used specificity level.
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Affiliation(s)
- Shiba Kuanar
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Jason Cai
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Hirotsugu Nakai
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Hiroki Nagayama
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA
- Department of Radiology, Nagasaki University, Nagasaki, Japan
| | | | - Jordan LeGout
- Department of Radiology, Mayo Clinic, Jacksonville, FL, USA
| | | | - Adam Froemming
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA
| | | | - Chandler Dora
- Department of Urology, Mayo Clinic, Jacksonville, FL, USA
| | | | - Jason Klug
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA
| | | | | | - Naoki Takahashi
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA.
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Fassia MK, Balasubramanian A, Woo S, Vargas HA, Hricak H, Konukoglu E, Becker AS. Deep Learning Prostate MRI Segmentation Accuracy and Robustness: A Systematic Review. Radiol Artif Intell 2024; 6:e230138. [PMID: 38568094 PMCID: PMC11294957 DOI: 10.1148/ryai.230138] [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: 04/26/2023] [Revised: 02/24/2024] [Accepted: 03/19/2024] [Indexed: 04/28/2024]
Abstract
Purpose To investigate the accuracy and robustness of prostate segmentation using deep learning across various training data sizes, MRI vendors, prostate zones, and testing methods relative to fellowship-trained diagnostic radiologists. Materials and Methods In this systematic review, Embase, PubMed, Scopus, and Web of Science databases were queried for English-language articles using keywords and related terms for prostate MRI segmentation and deep learning algorithms dated to July 31, 2022. A total of 691 articles from the search query were collected and subsequently filtered to 48 on the basis of predefined inclusion and exclusion criteria. Multiple characteristics were extracted from selected studies, such as deep learning algorithm performance, MRI vendor, and training dataset features. The primary outcome was comparison of mean Dice similarity coefficient (DSC) for prostate segmentation for deep learning algorithms versus diagnostic radiologists. Results Forty-eight studies were included. Most published deep learning algorithms for whole prostate gland segmentation (39 of 42 [93%]) had a DSC at or above expert level (DSC ≥ 0.86). The mean DSC was 0.79 ± 0.06 (SD) for peripheral zone, 0.87 ± 0.05 for transition zone, and 0.90 ± 0.04 for whole prostate gland segmentation. For selected studies that used one major MRI vendor, the mean DSCs of each were as follows: General Electric (three of 48 studies), 0.92 ± 0.03; Philips (four of 48 studies), 0.92 ± 0.02; and Siemens (six of 48 studies), 0.91 ± 0.03. Conclusion Deep learning algorithms for prostate MRI segmentation demonstrated accuracy similar to that of expert radiologists despite varying parameters; therefore, future research should shift toward evaluating segmentation robustness and patient outcomes across diverse clinical settings. Keywords: MRI, Genital/Reproductive, Prostate Segmentation, Deep Learning Systematic review registration link: osf.io/nxaev © RSNA, 2024.
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Affiliation(s)
- Mohammad-Kasim Fassia
- From the Departments of Radiology (M.K.F.) and Urology (A.B.), New York-Presbyterian Weill Cornell Medical Center, 525 E 68th St, New York, NY 10065-4870; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY (S.W., H.A.V., H.H., A.S.B.); and Department of Biomedical Imaging, ETH-Zurich, Zurich Switzerland (E.K.)
| | - Adithya Balasubramanian
- From the Departments of Radiology (M.K.F.) and Urology (A.B.), New York-Presbyterian Weill Cornell Medical Center, 525 E 68th St, New York, NY 10065-4870; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY (S.W., H.A.V., H.H., A.S.B.); and Department of Biomedical Imaging, ETH-Zurich, Zurich Switzerland (E.K.)
| | - Sungmin Woo
- From the Departments of Radiology (M.K.F.) and Urology (A.B.), New York-Presbyterian Weill Cornell Medical Center, 525 E 68th St, New York, NY 10065-4870; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY (S.W., H.A.V., H.H., A.S.B.); and Department of Biomedical Imaging, ETH-Zurich, Zurich Switzerland (E.K.)
| | - Hebert Alberto Vargas
- From the Departments of Radiology (M.K.F.) and Urology (A.B.), New York-Presbyterian Weill Cornell Medical Center, 525 E 68th St, New York, NY 10065-4870; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY (S.W., H.A.V., H.H., A.S.B.); and Department of Biomedical Imaging, ETH-Zurich, Zurich Switzerland (E.K.)
| | - Hedvig Hricak
- From the Departments of Radiology (M.K.F.) and Urology (A.B.), New York-Presbyterian Weill Cornell Medical Center, 525 E 68th St, New York, NY 10065-4870; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY (S.W., H.A.V., H.H., A.S.B.); and Department of Biomedical Imaging, ETH-Zurich, Zurich Switzerland (E.K.)
| | - Ender Konukoglu
- From the Departments of Radiology (M.K.F.) and Urology (A.B.), New York-Presbyterian Weill Cornell Medical Center, 525 E 68th St, New York, NY 10065-4870; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY (S.W., H.A.V., H.H., A.S.B.); and Department of Biomedical Imaging, ETH-Zurich, Zurich Switzerland (E.K.)
| | - Anton S. Becker
- From the Departments of Radiology (M.K.F.) and Urology (A.B.), New York-Presbyterian Weill Cornell Medical Center, 525 E 68th St, New York, NY 10065-4870; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY (S.W., H.A.V., H.H., A.S.B.); and Department of Biomedical Imaging, ETH-Zurich, Zurich Switzerland (E.K.)
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Xu L, Zhang G, Zhang D, Zhang J, Zhang X, Bai X, Chen L, Peng Q, Jin R, Mao L, Li X, Jin Z, Sun H. Development and clinical utility analysis of a prostate zonal segmentation model on T2-weighted imaging: a multicenter study. Insights Imaging 2023; 14:44. [PMID: 36928683 PMCID: PMC10020392 DOI: 10.1186/s13244-023-01394-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 02/19/2023] [Indexed: 03/18/2023] Open
Abstract
OBJECTIVES To automatically segment prostate central gland (CG) and peripheral zone (PZ) on T2-weighted imaging using deep learning and assess the model's clinical utility by comparing it with a radiologist annotation and analyzing relevant influencing factors, especially the prostate zonal volume. METHODS A 3D U-Net-based model was trained with 223 patients from one institution and tested using one internal testing group (n = 93) and two external testing datasets, including one public dataset (ETDpub, n = 141) and one private dataset from two centers (ETDpri, n = 59). The Dice similarity coefficients (DSCs), 95th Hausdorff distance (95HD), and average boundary distance (ABD) were calculated to evaluate the model's performance and further compared with a junior radiologist's performance in ETDpub. To investigate factors influencing the model performance, patients' clinical characteristics, prostate morphology, and image parameters in ETDpri were collected and analyzed using beta regression. RESULTS The DSCs in the internal testing group, ETDpub, and ETDpri were 0.909, 0.889, and 0.869 for CG, and 0.844, 0.755, and 0.764 for PZ, respectively. The mean 95HD and ABD were less than 7.0 and 1.3 for both zones. The U-Net model outperformed the junior radiologist, having a higher DSC (0.769 vs. 0.706) and higher intraclass correlation coefficient for volume estimation in PZ (0.836 vs. 0.668). CG volume and Magnetic Resonance (MR) vendor were significant influencing factors for CG and PZ segmentation. CONCLUSIONS The 3D U-Net model showed good performance for CG and PZ auto-segmentation in all the testing groups and outperformed the junior radiologist for PZ segmentation. The model performance was susceptible to prostate morphology and MR scanner parameters.
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Affiliation(s)
- Lili Xu
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No.1, Wangfujing Street, Dongcheng District, Beijing, 100730, China.,National Center for Quality Control of Radiology, Beijing, China
| | - Gumuyang Zhang
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No.1, Wangfujing Street, Dongcheng District, Beijing, 100730, China
| | - Daming Zhang
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No.1, Wangfujing Street, Dongcheng District, Beijing, 100730, China
| | - Jiahui Zhang
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No.1, Wangfujing Street, Dongcheng District, Beijing, 100730, China
| | - Xiaoxiao Zhang
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No.1, Wangfujing Street, Dongcheng District, Beijing, 100730, China
| | - Xin Bai
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No.1, Wangfujing Street, Dongcheng District, Beijing, 100730, China
| | - Li Chen
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No.1, Wangfujing Street, Dongcheng District, Beijing, 100730, China
| | - Qianyu Peng
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No.1, Wangfujing Street, Dongcheng District, Beijing, 100730, China
| | - Ru Jin
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No.1, Wangfujing Street, Dongcheng District, Beijing, 100730, China
| | - Li Mao
- AI Lab, Deepwise Healthcare, Beijing, China
| | - Xiuli Li
- AI Lab, Deepwise Healthcare, Beijing, China
| | - Zhengyu Jin
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No.1, Wangfujing Street, Dongcheng District, Beijing, 100730, China. .,National Center for Quality Control of Radiology, Beijing, China.
| | - Hao Sun
- Department of Radiology, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Shuaifuyuan No.1, Wangfujing Street, Dongcheng District, Beijing, 100730, China. .,National Center for Quality Control of Radiology, Beijing, China.
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Hung ALY, Zheng H, Miao Q, Raman SS, Terzopoulos D, Sung K. CAT-Net: A Cross-Slice Attention Transformer Model for Prostate Zonal Segmentation in MRI. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:291-303. [PMID: 36194719 PMCID: PMC10071136 DOI: 10.1109/tmi.2022.3211764] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Prostate cancer is the second leading cause of cancer death among men in the United States. The diagnosis of prostate MRI often relies on accurate prostate zonal segmentation. However, state-of-the-art automatic segmentation methods often fail to produce well-contained volumetric segmentation of the prostate zones since certain slices of prostate MRI, such as base and apex slices, are harder to segment than other slices. This difficulty can be overcome by leveraging important multi-scale image-based information from adjacent slices, but current methods do not fully learn and exploit such cross-slice information. In this paper, we propose a novel cross-slice attention mechanism, which we use in a Transformer module to systematically learn cross-slice information at multiple scales. The module can be utilized in any existing deep-learning-based segmentation framework with skip connections. Experiments show that our cross-slice attention is able to capture cross-slice information significant for prostate zonal segmentation in order to improve the performance of current state-of-the-art methods. Cross-slice attention improves segmentation accuracy in the peripheral zones, such that segmentation results are consistent across all the prostate slices (apex, mid-gland, and base). The code for the proposed model is available at https://bit.ly/CAT-Net.
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Wu C, Montagne S, Hamzaoui D, Ayache N, Delingette H, Renard-Penna R. Automatic segmentation of prostate zonal anatomy on MRI: a systematic review of the literature. Insights Imaging 2022; 13:202. [PMID: 36543901 PMCID: PMC9772373 DOI: 10.1186/s13244-022-01340-2] [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: 08/11/2022] [Accepted: 11/27/2022] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVES Accurate zonal segmentation of prostate boundaries on MRI is a critical prerequisite for automated prostate cancer detection based on PI-RADS. Many articles have been published describing deep learning methods offering great promise for fast and accurate segmentation of prostate zonal anatomy. The objective of this review was to provide a detailed analysis and comparison of applicability and efficiency of the published methods for automatic segmentation of prostate zonal anatomy by systematically reviewing the current literature. METHODS A Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) was conducted until June 30, 2021, using PubMed, ScienceDirect, Web of Science and EMBase databases. Risk of bias and applicability based on Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) criteria adjusted with Checklist for Artificial Intelligence in Medical Imaging (CLAIM) were assessed. RESULTS A total of 458 articles were identified, and 33 were included and reviewed. Only 2 articles had a low risk of bias for all four QUADAS-2 domains. In the remaining, insufficient details about database constitution and segmentation protocol provided sources of bias (inclusion criteria, MRI acquisition, ground truth). Eighteen different types of terminology for prostate zone segmentation were found, while 4 anatomic zones are described on MRI. Only 2 authors used a blinded reading, and 4 assessed inter-observer variability. CONCLUSIONS Our review identified numerous methodological flaws and underlined biases precluding us from performing quantitative analysis for this review. This implies low robustness and low applicability in clinical practice of the evaluated methods. Actually, there is not yet consensus on quality criteria for database constitution and zonal segmentation methodology.
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Affiliation(s)
- Carine Wu
- Sorbonne Université, Paris, France.
- Academic Department of Radiology, Hôpital Tenon, Assistance Publique des Hôpitaux de Paris, 4 Rue de La Chine, 75020, Paris, France.
| | - Sarah Montagne
- Sorbonne Université, Paris, France
- Academic Department of Radiology, Hôpital Tenon, Assistance Publique des Hôpitaux de Paris, 4 Rue de La Chine, 75020, Paris, France
- Academic Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique des Hôpitaux de Paris, Paris, France
- GRC N° 5, Oncotype-Uro, Sorbonne Université, Paris, France
| | - Dimitri Hamzaoui
- Inria, Epione Team, Sophia Antipolis, Université Côte d'Azur, Nice, France
| | - Nicholas Ayache
- Inria, Epione Team, Sophia Antipolis, Université Côte d'Azur, Nice, France
| | - Hervé Delingette
- Inria, Epione Team, Sophia Antipolis, Université Côte d'Azur, Nice, France
| | - Raphaële Renard-Penna
- Sorbonne Université, Paris, France
- Academic Department of Radiology, Hôpital Tenon, Assistance Publique des Hôpitaux de Paris, 4 Rue de La Chine, 75020, Paris, France
- Academic Department of Radiology, Hôpital Pitié-Salpétrière, Assistance Publique des Hôpitaux de Paris, Paris, France
- GRC N° 5, Oncotype-Uro, Sorbonne Université, Paris, France
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Artificial Intelligence Segmentation Algorithm-Based Optical Coherence Tomography Image in Evaluation of Binocular Retinopathy. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:3235504. [PMID: 35693270 PMCID: PMC9177319 DOI: 10.1155/2022/3235504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 05/06/2022] [Accepted: 05/09/2022] [Indexed: 11/25/2022]
Abstract
On account of optical coherence tomography (OCT) images with intelligent segmentation algorithm, this article investigated the clinical efficacy and safety of docetaxel combined with fluorouracil. In this study, 60 patients with retinopathy treated in hospital were selected as the research objects. There were 30 cases in each group, the control group was treated with conventional images, and the observation group was treated with algorithm-based OCT images. Intelligent segmentation boundary detection algorithm, boundary tracking, and contour localization were proposed and applied to the OCT images of patients to analyze features and measure corneal thickness in OCT images with high signal-to-noise ratio and noise and artifacts. Objects in the control group were treated with semiconductor laser, and those in the observation group were treated with OCT images with algorithm in addition to the treatment of the control group. The results showed that the number of images with relative error of 2 was more, and the number of images with relative error of -2 was the least. The average thickness of high-quality images was 562.7 μm, and the average thickness of images with noise and artifacts was 573.8 μm. The total effective rate of the observation group was 96.67%, which was significantly higher than that of the control group (80%), and the curative effect and physical improvement rate of the observation group were significantly better than that of the control group (P < 0.05). All in all, the feature extraction of OCT images and corneal measurement proposed in this study had a good measurement effect, and the method had the advantages of strong anti-interference ability and high measurement accuracy.
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Rouvière O, Moldovan PC, Vlachomitrou A, Gouttard S, Riche B, Groth A, Rabotnikov M, Ruffion A, Colombel M, Crouzet S, Weese J, Rabilloud M. Combined model-based and deep learning-based automated 3D zonal segmentation of the prostate on T2-weighted MR images: clinical evaluation. Eur Radiol 2022; 32:3248-3259. [PMID: 35001157 DOI: 10.1007/s00330-021-08408-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 09/28/2021] [Accepted: 10/09/2021] [Indexed: 11/04/2022]
Abstract
OBJECTIVE To train and to test for prostate zonal segmentation an existing algorithm already trained for whole-gland segmentation. METHODS The algorithm, combining model-based and deep learning-based approaches, was trained for zonal segmentation using the NCI-ISBI-2013 dataset and 70 T2-weighted datasets acquired at an academic centre. Test datasets were randomly selected among examinations performed at this centre on one of two scanners (General Electric, 1.5 T; Philips, 3 T) not used for training. Automated segmentations were corrected by two independent radiologists. When segmentation was initiated outside the prostate, images were cropped and segmentation repeated. Factors influencing the algorithm's mean Dice similarity coefficient (DSC) and its precision were assessed using beta regression. RESULTS Eighty-two test datasets were selected; one was excluded. In 13/81 datasets, segmentation started outside the prostate, but zonal segmentation was possible after image cropping. Depending on the radiologist chosen as reference, algorithm's median DSCs were 96.4/97.4%, 91.8/93.0% and 79.9/89.6% for whole-gland, central gland and anterior fibromuscular stroma (AFMS) segmentations, respectively. DSCs comparing radiologists' delineations were 95.8%, 93.6% and 81.7%, respectively. For all segmentation tasks, the scanner used for imaging significantly influenced the mean DSC and its precision, and the mean DSC was significantly lower in cases with initial segmentation outside the prostate. For central gland segmentation, the mean DSC was also significantly lower in larger prostates. The radiologist chosen as reference had no significant impact, except for AFMS segmentation. CONCLUSIONS The algorithm performance fell within the range of inter-reader variability but remained significantly impacted by the scanner used for imaging. KEY POINTS • Median Dice similarity coefficients obtained by the algorithm fell within human inter-reader variability for the three segmentation tasks (whole gland, central gland, anterior fibromuscular stroma). • The scanner used for imaging significantly impacted the performance of the automated segmentation for the three segmentation tasks. • The performance of the automated segmentation of the anterior fibromuscular stroma was highly variable across patients and showed also high variability across the two radiologists.
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Affiliation(s)
- Olivier Rouvière
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Pavillon B, 5 place d'Arsonval, F-69437, Lyon, France. .,Université de Lyon, F-69003, Lyon, France. .,Faculté de Médecine Lyon Est, Université Lyon 1, F-69003, Lyon, France. .,INSERM, LabTau, U1032, Lyon, France.
| | - Paul Cezar Moldovan
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Pavillon B, 5 place d'Arsonval, F-69437, Lyon, France
| | - Anna Vlachomitrou
- Philips France, 33 rue de Verdun, CS 60 055, 92156, Suresnes Cedex, France
| | - Sylvain Gouttard
- Department of Urinary and Vascular Imaging, Hôpital Edouard Herriot, Hospices Civils de Lyon, Pavillon B, 5 place d'Arsonval, F-69437, Lyon, France
| | - Benjamin Riche
- Service de Biostatistique Et Bioinformatique, Pôle Santé Publique, Hospices Civils de Lyon, F-69003, Lyon, France.,Laboratoire de Biométrie Et Biologie Évolutive, Équipe Biostatistique-Santé, UMR 5558, CNRS, F-69100, Villeurbanne, France
| | - Alexandra Groth
- Philips Research, Röntgenstrasse 24-26, 22335, Hamburg, Germany
| | | | - Alain Ruffion
- Department of Urology, Centre Hospitalier Lyon Sud, Hospices Civils de Lyon, F-69310, Pierre-Bénite, France
| | - Marc Colombel
- Université de Lyon, F-69003, Lyon, France.,Faculté de Médecine Lyon Est, Université Lyon 1, F-69003, Lyon, France.,Department of Urology, Hôpital Edouard Herriot, Hospices Civils de Lyon, F-69437, Lyon, France
| | - Sébastien Crouzet
- Department of Urology, Hôpital Edouard Herriot, Hospices Civils de Lyon, F-69437, Lyon, France
| | - Juergen Weese
- Philips Research, Röntgenstrasse 24-26, 22335, Hamburg, Germany
| | - Muriel Rabilloud
- Université de Lyon, F-69003, Lyon, France.,Faculté de Médecine Lyon Est, Université Lyon 1, F-69003, Lyon, France.,Service de Biostatistique Et Bioinformatique, Pôle Santé Publique, Hospices Civils de Lyon, F-69003, Lyon, France.,Laboratoire de Biométrie Et Biologie Évolutive, Équipe Biostatistique-Santé, UMR 5558, CNRS, F-69100, Villeurbanne, France
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Wahid KA, Ahmed S, He R, van Dijk LV, Teuwen J, McDonald BA, Salama V, Mohamed AS, Salzillo T, Dede C, Taku N, Lai SY, Fuller CD, Naser MA. Evaluation of deep learning-based multiparametric MRI oropharyngeal primary tumor auto-segmentation and investigation of input channel effects: Results from a prospective imaging registry. Clin Transl Radiat Oncol 2022; 32:6-14. [PMID: 34765748 PMCID: PMC8570930 DOI: 10.1016/j.ctro.2021.10.003] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 09/24/2021] [Accepted: 10/10/2021] [Indexed: 12/09/2022] Open
Abstract
BACKGROUND/PURPOSE Oropharyngeal cancer (OPC) primary gross tumor volume (GTVp) segmentation is crucial for radiotherapy. Multiparametric MRI (mpMRI) is increasingly used for OPC adaptive radiotherapy but relies on manual segmentation. Therefore, we constructed mpMRI deep learning (DL) OPC GTVp auto-segmentation models and determined the impact of input channels on segmentation performance. MATERIALS/METHODS GTVp ground truth segmentations were manually generated for 30 OPC patients from a clinical trial. We evaluated five mpMRI input channels (T2, T1, ADC, Ktrans, Ve). 3D Residual U-net models were developed and assessed using leave-one-out cross-validation. A baseline T2 model was compared to mpMRI models (T2 + T1, T2 + ADC, T2 + Ktrans, T2 + Ve, all five channels [ALL]) primarily using the Dice similarity coefficient (DSC). False-negative DSC (FND), false-positive DSC, sensitivity, positive predictive value, surface DSC, Hausdorff distance (HD), 95% HD, and mean surface distance were also assessed. For the best model, ground truth and DL-generated segmentations were compared through a blinded Turing test using three physician observers. RESULTS Models yielded mean DSCs from 0.71 ± 0.12 (ALL) to 0.73 ± 0.12 (T2 + T1). Compared to the T2 model, performance was significantly improved for FND, sensitivity, surface DSC, HD, and 95% HD for the T2 + T1 model (p < 0.05) and for FND for the T2 + Ve and ALL models (p < 0.05). No model demonstrated significant correlations between tumor size and DSC (p > 0.05). Most models demonstrated significant correlations between tumor size and HD or Surface DSC (p < 0.05), except those that included ADC or Ve as input channels (p > 0.05). On average, there were no significant differences between ground truth and DL-generated segmentations for all observers (p > 0.05). CONCLUSION DL using mpMRI provides reasonably accurate segmentations of OPC GTVp that may be comparable to ground truth segmentations generated by clinical experts. Incorporating additional mpMRI channels may increase the performance of FND, sensitivity, surface DSC, HD, and 95% HD, and improve model robustness to tumor size.
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Affiliation(s)
- Kareem A. Wahid
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Sara Ahmed
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Renjie He
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Lisanne V. van Dijk
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Jonas Teuwen
- Department of Medical Imaging, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Brigid A. McDonald
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Vivian Salama
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Abdallah S.R. Mohamed
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Travis Salzillo
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Cem Dede
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Nicolette Taku
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Stephen Y. Lai
- Department of Head and Neck Surgery, University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Clifton D. Fuller
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Mohamed A. Naser
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX USA
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Nai YH, Teo BW, Tan NL, O'Doherty S, Stephenson MC, Thian YL, Chiong E, Reilhac A. Comparison of metrics for the evaluation of medical segmentations using prostate MRI dataset. Comput Biol Med 2021; 134:104497. [PMID: 34022486 DOI: 10.1016/j.compbiomed.2021.104497] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 05/11/2021] [Accepted: 05/11/2021] [Indexed: 10/21/2022]
Abstract
Nine previously proposed segmentation evaluation metrics, targeting medical relevance, accounting for holes, and added regions or differentiating over- and under-segmentation, were compared with 24 traditional metrics to identify those which better capture the requirements for clinical segmentation evaluation. Evaluation was first performed using 2D synthetic shapes to highlight features and pitfalls of the metrics with known ground truths (GTs) and machine segmentations (MSs). Clinical evaluation was then performed using publicly-available prostate images of 20 subjects with MSs generated by 3 different deep learning networks (DenseVNet, HighRes3DNet, and ScaleNet) and GTs drawn by 2 readers. The same readers also performed the 2D visual assessment of the MSs using a dual negative-positive grading of -5 to 5 to reflect over- and under-estimation. Nine metrics that correlated well with visual assessment were selected for further evaluation using 3 different network ranking methods - based on a single metric, normalizing the metric using 2 GTs, and ranking the network based on a metric then averaging, including leave-one-out evaluation. These metrics yielded consistent ranking with HighRes3DNet ranked first then DenseVNet and ScaleNet using all ranking methods. Relative volume difference yielded the best positivity-agreement and correlation with dual visual assessment, and thus is better for providing over- and under-estimation. Interclass Correlation yielded the strongest correlation with the absolute visual assessment (0-5). Symmetric-boundary dice consistently yielded good discrimination of the networks for all three ranking methods with relatively small variations within network. Good rank discrimination may be an additional metric feature required for better network performance evaluation.
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Affiliation(s)
- Ying-Hwey Nai
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | | | - Nadya L Tan
- St. Joseph's Institution International, Singapore
| | - Sophie O'Doherty
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Mary C Stephenson
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Yee Liang Thian
- Department of Diagnostic Imaging, National University Hospital, Singapore
| | - Edmund Chiong
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Urology, National University Hospital, Singapore
| | - Anthonin Reilhac
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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