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Jeong S, Cheon W, Kim S, Park W, Han Y. Deep-learning-based segmentation using individual patient data on prostate cancer radiation therapy. PLoS One 2024; 19:e0308181. [PMID: 39083552 PMCID: PMC11290636 DOI: 10.1371/journal.pone.0308181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 07/17/2024] [Indexed: 08/02/2024] Open
Abstract
PURPOSE Organ-at-risk segmentation is essential in adaptive radiotherapy (ART). Learning-based automatic segmentation can reduce committed labor and accelerate the ART process. In this study, an auto-segmentation model was developed by employing individual patient datasets and a deep-learning-based augmentation method for tailoring radiation therapy according to the changes in the target and organ of interest in patients with prostate cancer. METHODS Two computed tomography (CT) datasets with well-defined labels, including contoured prostate, bladder, and rectum, were obtained from 18 patients. The labels of the CT images captured during radiation therapy (CT2nd) were predicted using CT images scanned before radiation therapy (CT1st). From the deformable vector fields (DVFs) created by using the VoxelMorph method, 10 DVFs were extracted when each of the modified CT and CT2nd images were deformed and registered to the fixed CT1st image. Augmented images were acquired by utilizing 110 extracted DVFs and spatially transforming the CT1st images and labels. An nnU-net autosegmentation network was trained by using the augmented images, and the CT2nd label was predicted. A patient-specific model was created for 18 patients, and the performances of the individual models were evaluated. The results were evaluated by employing the Dice similarity coefficient (DSC), average Hausdorff distance, and mean surface distance. The accuracy of the proposed model was compared with those of models trained with large datasets. RESULTS Patient-specific models were developed successfully. For the proposed method, the DSC values of the actual and predicted labels for the bladder, prostate, and rectum were 0.94 ± 0.03, 0.84 ± 0.07, and 0.83 ± 0.04, respectively. CONCLUSION We demonstrated the feasibility of automatic segmentation by employing individual patient datasets and image augmentation techniques. The proposed method has potential for clinical application in automatic prostate segmentation for ART.
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Affiliation(s)
- Sangwoon Jeong
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea
| | - Wonjoong Cheon
- Department of Radiation Oncology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Sungjin Kim
- Department of Radiation Oncology, Samsung Medical Center, Seoul, Korea
| | - Won Park
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Youngyih Han
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
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Kuisma A, Ranta I, Keyriläinen J, Suilamo S, Wright P, Pesola M, Warner L, Löyttyniemi E, Minn H. Validation of automated magnetic resonance image segmentation for radiation therapy planning in prostate cancer. PHYSICS & IMAGING IN RADIATION ONCOLOGY 2020; 13:14-20. [PMID: 33458302 PMCID: PMC7807774 DOI: 10.1016/j.phro.2020.02.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 12/23/2019] [Accepted: 02/24/2020] [Indexed: 01/06/2023]
Abstract
Background and purpose Magnetic resonance imaging (MRI) is increasingly used in radiation therapy planning of prostate cancer (PC) to reduce target volume delineation uncertainty. This study aimed to assess and validate the performance of a fully automated segmentation tool (AST) in MRI based radiation therapy planning of PC. Material and methods Pelvic structures of 65 PC patients delineated in an MRI-only workflow according to established guidelines were included in the analysis. Automatic vs manual segmentation by an experienced oncologist was compared with geometrical parameters, such as the dice similarity coefficient (DSC). Fifteen patients had a second MRI within 15 days to assess repeatability of the AST for prostate and seminal vesicles. Furthermore, we investigated whether hormonal therapy or body mass index (BMI) affected the AST results. Results The AST showed high agreement with manual segmentation expressed as DSC (mean, SD) for delineating prostate (0.84, 0.04), bladder (0.92, 0.04) and rectum (0.86, 0.04). For seminal vesicles (0.56, 0.17) and penile bulb (0.69, 0.12) the respective agreement was moderate. Performance of AST was not influenced by neoadjuvant hormonal therapy, although those on treatment had significantly smaller prostates than the hormone-naïve patients (p < 0.0001). In repeat assessment, consistency of prostate delineation resulted in mean DSC of 0.89, (SD 0.03) between the paired MRI scans for AST, while mean DSC of manual delineation was 0.82, (SD 0.05). Conclusion Fully automated MRI segmentation tool showed good agreement and repeatability compared with manual segmentation and was found clinically robust in patients with PC. However, manual review and adjustment of some structures in individual cases remain important in clinical use.
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Affiliation(s)
- Anna Kuisma
- Turku University Hospital, Department of Oncology and Radiotherapy, Hämeentie 11, FI-20521 Turku, Finland
| | - Iiro Ranta
- Turku University Hospital, Department of Oncology and Radiotherapy, Hämeentie 11, FI-20521 Turku, Finland.,Turku University Hospital, Department of Medical Physics, Hämeentie 11, FI-20521 Turku, Finland.,University of Turku, Department of Physics and Astronomy, Vesilinnantie 5, FI-20014 University of Turku, Finland
| | - Jani Keyriläinen
- Turku University Hospital, Department of Oncology and Radiotherapy, Hämeentie 11, FI-20521 Turku, Finland.,Turku University Hospital, Department of Medical Physics, Hämeentie 11, FI-20521 Turku, Finland.,University of Turku, Department of Physics and Astronomy, Vesilinnantie 5, FI-20014 University of Turku, Finland
| | - Sami Suilamo
- Turku University Hospital, Department of Oncology and Radiotherapy, Hämeentie 11, FI-20521 Turku, Finland.,Turku University Hospital, Department of Medical Physics, Hämeentie 11, FI-20521 Turku, Finland
| | - Pauliina Wright
- Turku University Hospital, Department of Oncology and Radiotherapy, Hämeentie 11, FI-20521 Turku, Finland.,Turku University Hospital, Department of Medical Physics, Hämeentie 11, FI-20521 Turku, Finland
| | - Marko Pesola
- Philips MR Therapy Oy, Äyritie 4, FI-01510 Vantaa, Finland
| | - Lizette Warner
- Philips MR Oncology, 3000 Minuteman Road, Andover, MA 01810, United States
| | - Eliisa Löyttyniemi
- University of Turku, Department of Biostatistics, Kiinamyllynkatu 10, FI-20014 University of Turku, Finland
| | - Heikki Minn
- Turku University Hospital, Department of Oncology and Radiotherapy, Hämeentie 11, FI-20521 Turku, Finland
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Yepes-Calderon F, Hwang D, Johnson R, Bhushan D, Gajawelli N, Yong S, Quinn B, Yap F, Gill I, Lepore N, Duddalwar V. EdgeRunner: a novel shape-based pipeline for tumours analysis and characterisation. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION 2016. [DOI: 10.1080/21681163.2016.1177797] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Fernando Yepes-Calderon
- Department of Radiology, Keck School of Medicine – USC, Los Angeles, CA, USA
- Children Hospital Los Angeles, Los Angeles, CA, USA
| | - Darryl Hwang
- Department of Radiology, Keck School of Medicine – USC, Los Angeles, CA, USA
| | - Rebecca Johnson
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
| | - Desai Bhushan
- Department of Radiology, Keck School of Medicine – USC, Los Angeles, CA, USA
| | - Niharika Gajawelli
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
| | - Steven Yong
- Department of Radiology, Keck School of Medicine – USC, Los Angeles, CA, USA
| | - Brian Quinn
- Department of Radiology, Keck School of Medicine – USC, Los Angeles, CA, USA
| | - Felix Yap
- Department of Radiology, Keck School of Medicine – USC, Los Angeles, CA, USA
| | | | - Natasha Lepore
- Children Hospital Los Angeles, Los Angeles, CA, USA
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
| | - Vinay Duddalwar
- Department of Radiology, Keck School of Medicine – USC, Los Angeles, CA, USA
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Tian Z, Liu L, Fei B. A fully automatic multi-atlas based segmentation method for prostate MR images. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2015; 9413:941340. [PMID: 26798187 PMCID: PMC4717836 DOI: 10.1117/12.2082229] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Most of multi-atlas segmentation methods focus on the registration between the full-size volumes of the data set. Although the transformations obtained from these registrations may be accurate for the global field of view of the images, they may not be accurate for the local prostate region. This is because different magnetic resonance (MR) images have different fields of view and may have large anatomical variability around the prostate. To overcome this limitation, we proposed a two-stage prostate segmentation method based on a fully automatic multi-atlas framework, which includes the detection stage i.e. locating the prostate, and the segmentation stage i.e. extracting the prostate. The purpose of the first stage is to find a cuboid that contains the whole prostate as small cubage as possible. In this paper, the cuboid including the prostate is detected by registering atlas edge volumes to the target volume while an edge detection algorithm is applied to every slice in the volumes. At the second stage, the proposed method focuses on the registration in the region of the prostate vicinity, which can improve the accuracy of the prostate segmentation. We evaluated the proposed method on 12 patient MR volumes by performing a leave-one-out study. Dice similarity coefficient (DSC) and Hausdorff distance (HD) are used to quantify the difference between our method and the manual ground truth. The proposed method yielded a DSC of 83.4%±4.3%, and a HD of 9.3 mm±2.6 mm. The fully automated segmentation method can provide a useful tool in many prostate imaging applications.
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Affiliation(s)
- Zhiqiang Tian
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA
| | - LiZhi Liu
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA
| | - Baowei Fei
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology
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