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Abbani N, Baudier T, Rit S, Franco FD, Okoli F, Jaouen V, Tilquin F, Barateau A, Simon A, de Crevoisier R, Bert J, Sarrut D. Deep learning-based segmentation in prostate radiation therapy using Monte Carlo simulated cone-beam computed tomography. Med Phys 2022; 49:6930-6944. [PMID: 36000762 DOI: 10.1002/mp.15946] [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: 02/07/2022] [Revised: 07/28/2022] [Accepted: 08/05/2022] [Indexed: 12/13/2022] Open
Abstract
PURPOSE Segmenting organs in cone-beam CT (CBCT) images would allow to adapt the radiotherapy based on the organ deformations that may occur between treatment fractions. However, this is a difficult task because of the relative lack of contrast in CBCT images, leading to high inter-observer variability. Deformable image registration (DIR) and deep-learning based automatic segmentation approaches have shown interesting results for this task in the past years. However, they are either sensitive to large organ deformations, or require to train a convolutional neural network (CNN) from a database of delineated CBCT images, which is difficult to do without improvement of image quality. In this work, we propose an alternative approach: to train a CNN (using a deep learning-based segmentation tool called nnU-Net) from a database of artificial CBCT images simulated from planning CT, for which it is easier to obtain the organ contours. METHODS Pseudo-CBCT (pCBCT) images were simulated from readily available segmented planning CT images, using the GATE Monte Carlo simulation. CT reference delineations were copied onto the pCBCT, resulting in a database of segmented images used to train the neural network. The studied segmentation contours were: bladder, rectum, and prostate contours. We trained multiple nnU-Net models using different training: (1) segmented real CBCT, (2) pCBCT, (3) segmented real CT and tested on pseudo-CT (pCT) generated from CBCT with cycleGAN, and (4) a combination of (2) and (3). The evaluation was performed on different datasets of segmented CBCT or pCT by comparing predicted segmentations with reference ones thanks to Dice similarity score and Hausdorff distance. A qualitative evaluation was also performed to compare DIR-based and nnU-Net-based segmentations. RESULTS Training with pCBCT was found to lead to comparable results to using real CBCT images. When evaluated on CBCT obtained from the same hospital as the CT images used in the simulation of the pCBCT, the model trained with pCBCT scored mean DSCs of 0.92 ± 0.05, 0.87 ± 0.02, and 0.85 ± 0.04 and mean Hausdorff distance 4.67 ± 3.01, 3.91 ± 0.98, and 5.00 ± 1.32 for the bladder, rectum, and prostate contours respectively, while the model trained with real CBCT scored mean DSCs of 0.91 ± 0.06, 0.83 ± 0.07, and 0.81 ± 0.05 and mean Hausdorff distance 5.62 ± 3.24, 6.43 ± 5.11, and 6.19 ± 1.14 for the bladder, rectum, and prostate contours, respectively. It was also found to outperform models using pCT or a combination of both, except for the prostate contour when tested on a dataset from a different hospital. Moreover, the resulting segmentations demonstrated a clinical acceptability, where 78% of bladder segmentations, 98% of rectum segmentations, and 93% of prostate segmentations required minor or no corrections, and for 76% of the patients, all structures of the patient required minor or no corrections. CONCLUSION We proposed to use simulated CBCT images to train a nnU-Net segmentation model, avoiding the need to gather complex and time-consuming reference delineations on CBCT images.
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Affiliation(s)
- Nelly Abbani
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, Lyon, France
| | - Thomas Baudier
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, Lyon, France
| | - Simon Rit
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, Lyon, France
| | - Francesca di Franco
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, Lyon, France
| | - Franklin Okoli
- LaTIM, Université de Bretagne Occidentale, Inserm, Brest, France
| | - Vincent Jaouen
- LaTIM, Université de Bretagne Occidentale, Inserm, Brest, France
| | | | - Anaïs Barateau
- Univ Rennes, CLCC Eugène Marquis, Inserm, Rennes, France
| | - Antoine Simon
- Univ Rennes, CLCC Eugène Marquis, Inserm, Rennes, France
| | | | - Julien Bert
- LaTIM, Université de Bretagne Occidentale, Inserm, Brest, France
| | - David Sarrut
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, Lyon, France
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Vilches-Freixas G, Létang JM, Ducros N, Rit S. Optimization of dual-energy CT acquisitions for proton therapy using projection-based decomposition. Med Phys 2017; 44:4548-4558. [DOI: 10.1002/mp.12448] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Revised: 06/21/2017] [Accepted: 06/26/2017] [Indexed: 11/10/2022] Open
Affiliation(s)
- Gloria Vilches-Freixas
- Université de Lyon; CREATIS; CNRS UMR5220; Inserm U1206; INSA-Lyon; Université Lyon 1; Centre Léon Bérard; Lyon France
| | - Jean Michel Létang
- Université de Lyon; CREATIS; CNRS UMR5220; Inserm U1206; INSA-Lyon; Université Lyon 1; Centre Léon Bérard; Lyon France
| | - Nicolas Ducros
- Université de Lyon; CREATIS; CNRS UMR5220; Inserm U1206; INSA-Lyon; Université Lyon 1; Centre Léon Bérard; Lyon France
| | - Simon Rit
- Université de Lyon; CREATIS; CNRS UMR5220; Inserm U1206; INSA-Lyon; Université Lyon 1; Centre Léon Bérard; Lyon France
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Zöllner C, Rit S, Kurz C, Vilches-Freixas G, Kamp F, Dedes G, Belka C, Parodi K, Landry G. Decomposing a prior-CT-based cone-beam CT projection correction algorithm into scatter and beam hardening components. PHYSICS & IMAGING IN RADIATION ONCOLOGY 2017. [DOI: 10.1016/j.phro.2017.09.002] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Vilches-Freixas G, Létang JM, Brousmiche S, Romero E, Vila Oliva M, Kellner D, Deutschmann H, Keuschnigg P, Steininger P, Rit S. Technical Note: Procedure for the calibration and validation of kilo-voltage cone-beam CT models. Med Phys 2016; 43:5199. [DOI: 10.1118/1.4961400] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
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