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Popat M, Patel S. Research perspective and review towards brain tumour segmentation and classification using different image modalities. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2022. [DOI: 10.1080/21681163.2022.2124546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Mayuri Popat
- U & P.U. Patel Department of Computer Engineering, Chandubhai S Patel Institute of Technology (CSPIT), Charotar University of Science and Technology (CHARUSAT), Gujarat, India
| | - Sanskruti Patel
- Smt. Chandaben Mohanbhai Patel Institute of Computer Applications (CMPICA), Charotar University of Science and Technology (CHARUSAT), Gujarat, India
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Crouzen JA, Petoukhova AL, Wiggenraad RGJ, Hutschemaekers S, Gadellaa-van Hooijdonk CGM, van der Voort van Zyp NCMG, Mast ME, Zindler JD. Development and evaluation of an automated EPTN-consensus based organ at risk atlas in the brain on MRI. Radiother Oncol 2022; 173:262-268. [PMID: 35714807 DOI: 10.1016/j.radonc.2022.06.004] [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: 12/03/2021] [Revised: 04/29/2022] [Accepted: 06/08/2022] [Indexed: 11/19/2022]
Abstract
BACKGROUND AND PURPOSE During radiotherapy treatment planning, avoidance of organs at risk (OARs) is important. An international consensus-based delineation guideline was recently published with 34 OARs in the brain. We developed an MR-based OAR autosegmentation atlas and evaluated its performance compared to manual delineation. MATERIALS AND METHODS Anonymized cerebral T1-weighted MR scans (voxel size 0.9x0.9x0.9mm 3) were available. OARs were manually delineated according to international consensus. Fifty MR scans were used to develop the autosegmentation atlas in a commercially available treatment planning system (Raystation®). The performance of this atlas was tested on another 40 MR scans by automatically delineating 34 OARs, as defined by the 2018 EPTN consensus. Spatial overlap between manual and automated delineations was determined by calculating the Dice similarity coefficient (DSC). Two radiation oncologists determined the quality of each automatically delineated OAR. The time needed to delineate all OARs manually or to adjust automatically delineated OARs was determined. RESULTS DSC was ≥0.75 in 31 (91%) out of 34 automated OAR delineations. Delineations were rated by radiation oncologists as excellent or good in 29 (85%) out 34 OAR delineations, while 4 were rated fair (12%) and 1 was rated poor (3%). Interobserver agreement between the radiation oncologists ranged from 77-100% per OAR. The time to manually delineate all OARs was 88.5 minutes, while the time needed to adjust automatically delineated OARs was 15.8 minutes. CONCLUSION Autosegmentation of OARs enables high-quality contouring within a limited time. Accurate OAR delineation helps to define OAR constraints to mitigate serious complications and helps with the development of NTCP models.
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Affiliation(s)
- Jeroen A Crouzen
- Haaglanden Medical Center, Department of Radiotherapy, BA Leidschendam, The Netherlands.
| | - Anna L Petoukhova
- Haaglanden Medical Center, Department of Medical Physics, BA Leidschendam, The Netherlands.
| | - Ruud G J Wiggenraad
- Haaglanden Medical Center, Department of Radiotherapy, BA Leidschendam, The Netherlands
| | - Stefan Hutschemaekers
- Haaglanden Medical Center, Department of Radiotherapy, BA Leidschendam, The Netherlands.
| | | | | | - Mirjam E Mast
- Haaglanden Medical Center, Department of Radiotherapy, BA Leidschendam, The Netherlands.
| | - Jaap D Zindler
- Haaglanden Medical Center, Department of Radiotherapy, BA Leidschendam, The Netherlands.
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Yakar M, Etiz D. Artificial intelligence in radiation oncology. Artif Intell Med Imaging 2021; 2:13-31. [DOI: 10.35711/aimi.v2.i2.13] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 03/30/2021] [Accepted: 04/20/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is a computer science that tries to mimic human-like intelligence in machines that use computer software and algorithms to perform specific tasks without direct human input. Machine learning (ML) is a subunit of AI that uses data-driven algorithms that learn to imitate human behavior based on a previous example or experience. Deep learning is an ML technique that uses deep neural networks to create a model. The growth and sharing of data, increasing computing power, and developments in AI have initiated a transformation in healthcare. Advances in radiation oncology have produced a significant amount of data that must be integrated with computed tomography imaging, dosimetry, and imaging performed before each fraction. Of the many algorithms used in radiation oncology, has advantages and limitations with different computational power requirements. The aim of this review is to summarize the radiotherapy (RT) process in workflow order by identifying specific areas in which quality and efficiency can be improved by ML. The RT stage is divided into seven stages: patient evaluation, simulation, contouring, planning, quality control, treatment application, and patient follow-up. A systematic evaluation of the applicability, limitations, and advantages of AI algorithms has been done for each stage.
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Affiliation(s)
- Melek Yakar
- Department of Radiation Oncology, Eskisehir Osmangazi University Faculty of Medicine, Eskisehir 26040, Turkey
- Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir Osmangazi University, Eskisehir 26040, Turkey
| | - Durmus Etiz
- Department of Radiation Oncology, Eskisehir Osmangazi University Faculty of Medicine, Eskisehir 26040, Turkey
- Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir Osmangazi University, Eskisehir 26040, Turkey
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Xu L, Hu J, Song Y, Bai S, Yi Z. Clinical target volume segmentation for stomach cancer by stochastic width deep neural network. Med Phys 2021; 48:1720-1730. [PMID: 33503270 DOI: 10.1002/mp.14733] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 12/17/2020] [Accepted: 01/11/2021] [Indexed: 02/05/2023] Open
Abstract
PURPOSE Precise segmentation of clinical target volume (CTV) is the key to stomach cancer radiotherapy. We proposed a novel stochastic width-deep neural network (SW-DNN) for better automatically contouring stomach CTV. METHODS Stochastic width-deep neural network was an end-to-end approach, of which the core component was a novel SW mechanism that employed shortcut connections between the encoder and decoder in a random manner, and thus the width of the SW-DNN was stochastically adjustable to obtain improved segmentation results. In total, 150 stomach cancer patient computed tomography (CT) cases with the corresponding CTV labels were collected and used to train and evaluate the SW-DNN. Three common quantitative measures: true positive volume fraction (TPVF), positive predictive value (PPV), and Dice similarity coefficient (DSC) were used to evaluate the segmentation accuracy. RESULTS Clinical target volumes calculated by SW-DNN had significant quantitative advantages over three state-of-the-art methods. The average DSC value of SW-DNN was 2.1%, 2.8%, and 3.6% higher than that of three state-of-the-art methods. The average DSC, TPVF, and PPV values of SW-DNN were 2.1%, 4.0%, and 0.3% higher than that of the corresponding constant width DNN. CONCLUSIONS Stochastic width-deep neural network provided better performance for contouring stomach cancer CTV accurately and efficiently. It is a promising solution in clinical radiotherapy planning for stomach cancer.
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Affiliation(s)
- Lei Xu
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, 610065, P R China
| | - Junjie Hu
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, 610065, P R China
| | - Ying Song
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, 610065, P R China.,Department of Radiotherapy, West China Hospital, Sichuan University, Chengdu, 610065, P R China
| | - Sen Bai
- Department of Radiotherapy, West China Hospital, Sichuan University, Chengdu, 610065, P R China
| | - Zhang Yi
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, 610065, P R China
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Vogin G, Hettal L, Bartau C, Thariat J, Claeys MV, Peyraga G, Retif P, Schick U, Antoni D, Bodgal Z, Dhermain F, Feuvret L. Cranial organs at risk delineation: heterogenous practices in radiotherapy planning. Radiat Oncol 2021; 16:26. [PMID: 33541394 PMCID: PMC7863275 DOI: 10.1186/s13014-021-01756-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Accepted: 01/28/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Segmentation is a crucial step in treatment planning that directly impacts dose distribution and optimization. The aim of this study was to evaluate the inter-individual variability of common cranial organs at risk (OAR) delineation in neurooncology practice. METHODS Anonymized simulation contrast-enhanced CT and MR scans of one patient with a solitary brain metastasis was used for delineation and analysis. Expert professionals from 16 radiotherapy centers involved in brain structures delineation were asked to segment 9 OAR on their own treatment planning system. As reference, two experts in neurooncology, produced a unique consensual contour set according to guidelines. Overlap ratio, Kappa index (KI), volumetric ratio, Commonly Contoured Volume, Supplementary Contoured Volume were evaluated using Artiview™ v 2.8.2-according to occupation, seniority and level of expertise of all participants. RESULTS For the most frequently delineated and largest OAR, the mean KI are often good (0.8 for the parotid and the brainstem); however, for the smaller OAR, KI degrade (0.3 for the optic chiasm, 0.5% for the cochlea), with a significant discrimination (p < 0.01). The radiation oncologists, members of Association des Neuro-Oncologue d'Expression Française society performed better in all indicators compared to non-members (p < 0.01). Our exercise was effective in separating the different participating centers with 3 of the reported indicators (p < 0.01). CONCLUSION Our study illustrates the heterogeneity in normal structures contouring between professionals. We emphasize the need for cerebral OAR delineation harmonization-that is a major determinant of therapeutic ratio and clinical trials evaluation.
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Affiliation(s)
- Guillaume Vogin
- Department of Radiation Oncology, Institut de Cancérologie de Lorraine, Vandoeuvre Les Nancy, France
- IMoPA, UMR 7365 CNRS-Université de Lorraine, Vandoeuvre Les Nancy, France
- Centre National de radiothérapie du Grand-Duché de Luxembourg, Centre François Baclesse, Boîte postale 436, 4005 Esch sur Alzette, Luxembourg
| | - Liza Hettal
- IMoPA, UMR 7365 CNRS-Université de Lorraine, Vandoeuvre Les Nancy, France
| | - Clarisse Bartau
- Aquilab SAS, Parc Eurasanté - 250 rue Salvador Allende, Loos, France
| | - Juliette Thariat
- Département de Radiothérapie, Centre François Baclesse/ARCHADE, 3 Av General Harris, Caen, France
- Laboratoire de Physique Corpusculaire IN2P3/ENSICAEN - UMR6534 - Unicaen, Normandie Université, Caen, France
| | | | - Guillaume Peyraga
- Service de Radiothérapie, Institut Universitaire du Cancer de Toulouse (Oncopole), Toulouse, France
| | - Paul Retif
- Service de Radiothérapie, CHR de Metz-Thionville Site Mercy, Metz, France
| | - Ulrike Schick
- Département de radiothérapie, CHU de Brest, Brest, France
| | - Delphine Antoni
- Département de radiothérapie, Institut de Cancérologie Strasbourg Europe (ICANS), Strasbourg, France
| | - Zsuzsa Bodgal
- Centre National de radiothérapie du Grand-Duché de Luxembourg, Centre François Baclesse, Boîte postale 436, 4005 Esch sur Alzette, Luxembourg
| | - Frederic Dhermain
- Radiation Oncology Department, Gustave Roussy University Hospital, Villejuif, France
| | - Loic Feuvret
- Department of Radiation Oncology, AP-HP, Hôpitaux Universitaires La Pitié Salpêtrière - Charles Foix, Sorbonne Université, Paris, France
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Hu J, Song Y, Zhang L, Bai S, Yi Z. Multi-scale attention U-net for segmenting clinical target volume in graves’ ophthalmopathy. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.11.028] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Ding Y, Gong L, Zhang M, Li C, Qin Z. A multi-path adaptive fusion network for multimodal brain tumor segmentation. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.06.078] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Jarrett D, Stride E, Vallis K, Gooding MJ. Applications and limitations of machine learning in radiation oncology. Br J Radiol 2019; 92:20190001. [PMID: 31112393 PMCID: PMC6724618 DOI: 10.1259/bjr.20190001] [Citation(s) in RCA: 76] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Machine learning approaches to problem-solving are growing rapidly within healthcare, and radiation oncology is no exception. With the burgeoning interest in machine learning comes the significant risk of misaligned expectations as to what it can and cannot accomplish. This paper evaluates the role of machine learning and the problems it solves within the context of current clinical challenges in radiation oncology. The role of learning algorithms within the workflow for external beam radiation therapy are surveyed, considering simulation imaging, multimodal fusion, image segmentation, treatment planning, quality assurance, and treatment delivery and adaptation. For each aspect, the clinical challenges faced, the learning algorithms proposed, and the successes and limitations of various approaches are analyzed. It is observed that machine learning has largely thrived on reproducibly mimicking conventional human-driven solutions with more efficiency and consistency. On the other hand, since algorithms are generally trained using expert opinion as ground truth, machine learning is of limited utility where problems or ground truths are not well-defined, or if suitable measures of correctness are not available. As a result, machines may excel at replicating, automating and standardizing human behaviour on manual chores, meanwhile the conceptual clinical challenges relating to definition, evaluation, and judgement remain in the realm of human intelligence and insight.
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Affiliation(s)
- Daniel Jarrett
- 1 Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, UK.,2 Mirada Medical Ltd, Oxford, UK
| | - Eleanor Stride
- 1 Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, UK
| | - Katherine Vallis
- 3 Department of Oncology, Oxford Institute for Radiation Oncology, University of Oxford, UK
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Assessment of a guideline-based heart substructures delineation in left-sided breast cancer patients undergoing adjuvant radiotherapy : Quality assessment within a randomized phase III trial testing a cardioprotective treatment strategy (SAFE-2014). Strahlenther Onkol 2018; 195:43-51. [PMID: 30406290 DOI: 10.1007/s00066-018-1388-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Accepted: 10/17/2018] [Indexed: 12/25/2022]
Abstract
BACKGROUND AND PURPOSE In our institute, breast cancer patients undergoing adjuvant treatment are included in a protocol aimed to reduce cardiovascular morbidity (SAFE-2014, NCT2236806), assessing preclinical heart damage with heart speckle-tracking ultrasound. To develop a dose constraint related to subclinical heart damage, a reliable delineation of heart substructures based on a pre-existing guideline was made. PATIENTS AND METHODS Heart substructures of 16 left-sided breast cancer patients included in the SAFE protocol were delineated by five operators. For each substructure, a multi-contour delineation based on a majority vote algorithm (MCD) was created. A consensus-based delineation (CBD) was developed by an independent team of two blinded operators. Dice similarity coefficients (DSC) between volumes delineated by different operators and the MCD were collected and reported, as well as DSC between CBD and MCD. RESULTS Mean DSCs between heart chambers delineated by each operator and the corresponding MCDs ranged between 0.78 and 0.96. Mean DSC between substructures delineated by all single operators and the corresponding MCD ranged between 0.84 and 0.94. Mean DSC between CBD and the corresponding MCD ranged from 0.89 to 0.97. CONCLUSION Results showed low inter-observer variability of heart substructure delineation. This constitutes an external validation of the contouring atlas used, allowing a reliable dosimetric assessment of these volumes within the SAFE-2014 trial.
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3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study. Neuroimage 2018; 170:456-470. [DOI: 10.1016/j.neuroimage.2017.04.039] [Citation(s) in RCA: 219] [Impact Index Per Article: 36.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2016] [Revised: 02/23/2017] [Accepted: 04/17/2017] [Indexed: 01/08/2023] Open
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Raju AR, Suresh P, Rao RR. Bayesian HCS-based multi-SVNN: A classification approach for brain tumor segmentation and classification using Bayesian fuzzy clustering. Biocybern Biomed Eng 2018. [DOI: 10.1016/j.bbe.2018.05.001] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Men K, Dai J, Li Y. Automatic segmentation of the clinical target volume and organs at risk in the planning CT for rectal cancer using deep dilated convolutional neural networks. Med Phys 2017; 44:6377-6389. [PMID: 28963779 DOI: 10.1002/mp.12602] [Citation(s) in RCA: 196] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2017] [Revised: 09/21/2017] [Accepted: 09/22/2017] [Indexed: 12/13/2022] Open
Abstract
PURPOSE Delineation of the clinical target volume (CTV) and organs at risk (OARs) is very important for radiotherapy but is time-consuming and prone to inter-observer variation. Here, we proposed a novel deep dilated convolutional neural network (DDCNN)-based method for fast and consistent auto-segmentation of these structures. METHODS Our DDCNN method was an end-to-end architecture enabling fast training and testing. Specifically, it employed a novel multiple-scale convolutional architecture to extract multiple-scale context features in the early layers, which contain the original information on fine texture and boundaries and which are very useful for accurate auto-segmentation. In addition, it enlarged the receptive fields of dilated convolutions at the end of networks to capture complementary context features. Then, it replaced the fully connected layers with fully convolutional layers to achieve pixel-wise segmentation. We used data from 278 patients with rectal cancer for evaluation. The CTV and OARs were delineated and validated by senior radiation oncologists in the planning computed tomography (CT) images. A total of 218 patients chosen randomly were used for training, and the remaining 60 for validation. The Dice similarity coefficient (DSC) was used to measure segmentation accuracy. RESULTS Performance was evaluated on segmentation of the CTV and OARs. In addition, the performance of DDCNN was compared with that of U-Net. The proposed DDCNN method outperformed the U-Net for all segmentations, and the average DSC value of DDCNN was 3.8% higher than that of U-Net. Mean DSC values of DDCNN were 87.7% for the CTV, 93.4% for the bladder, 92.1% for the left femoral head, 92.3% for the right femoral head, 65.3% for the intestine, and 61.8% for the colon. The test time was 45 s per patient for segmentation of all the CTV, bladder, left and right femoral heads, colon, and intestine. We also assessed our approaches and results with those in the literature: our system showed superior performance and faster speed. CONCLUSIONS These data suggest that DDCNN can be used to segment the CTV and OARs accurately and efficiently. It was invariant to the body size, body shape, and age of the patients. DDCNN could improve the consistency of contouring and streamline radiotherapy workflows.
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Affiliation(s)
- Kuo Men
- National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jianrong Dai
- National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yexiong Li
- National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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