Technical Note: More accurate and efficient segmentation of organs‐at‐risk in radiotherapy with convolutional neural networks cascades

Men, Kuo; Geng, H.; Cheng, Charles Ching-An; Zhong, Haoyu; Huang, Mingyan; Fan, Yong; Plastaras, John P.; Lin, Alexander; Xiao, Ying

doi:10.1002/mp.13296

Cited by 53 publications

(57 citation statements)

References 34 publications

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“…Parotid glands DC (%) 92 AE 4, 37 91 AE 2, 75 88 AE 2, 46 91 m;f (N), 45 88, 53 87 AE 3(N), 60 87 AE 4 (N), 24 87, 64 86 AE 2 (N), 40 86 AE 3, 48 86 AE 4, 24 86 AE 5 (N), 31 86 AE 5, 42 86 AE 5, 40 86 AE 7, 93 91 m;f , 72 85 AE 2, 83 85 AE 3, 26 85 AE 4, 91 85 AE 4, 30 85 AE 5, 47 91 m;f (DL), 29 85, 60 84 AE 3, 34 84 AE 3 (N), 55 84 AE 4 (•), 60 84 AE 7 (N,IM), 66 84, 76 91 m;f , 22 91 m;f , 23 83 AE 2, 50 83 AE 3, 58 83 AE 5 (•), 36 83 AE 5, 86 83 AE 6, 36 83 AE 6 (N), 56 91 m;f , 95 91 m;f , 81 AE 4 (N), 70 81 AE 5, 28 81 AE 8 (N), 49 81 AE 8, 27 81 (N), 54 91 m;f , 52 91 m;f (ABAS), 29 79 (MR), 68 91 m;f , 77 79, 87 79, 57 77 AE 6, 65 91 m;f (N), 69 91 m;f (N), 35 76 AE 6, 63 76 (CT), 68 91 m;f , 35 75, 51 72 AE 10, …”

Section: Resultsmentioning

confidence: 99%

“…Eyeballs and vitreous humor (VH) DC (%) 96 AE 1 (VH), 97 95, 60 95 AE 2, 43 94, 33 91 m;f (DL), 29 93 AE 1, 48 93 AE 4, 47 92 AE 2 (•), 60 92 AE 2, 30 91 AE 2 (•), 36 ASD (mm) ASD91 m;f : 1.0 (VH), 99 1.2(0.9,1.8) (VH) 98 ; ASD91 m;f : 0.6(0.8) (+eye muscles) 79 ; DTA91 m;f : 2.0 (MR), 68 Optic chiasm DC (%) 91 m;f , 88 71 AE 9, 43 64 AE 16, 30 62 AE 17, 27 61 AE 6 (N), 24 59 AE 7, 40 59 AE 10 (N), 40 59 AE 14, 24 58 AE 10 (N), 55 58 AE 17 (N), 54 57 AE 13 (N, UB), 66 91 m;f , 73 53 AE 15, 46 52 AE 11 (N), 70 91 m;f , 22 45 AE 17 (N), 31 42 AE 17 (N), 49 91 m;f , 38 41(0,58), 94 41 AE 14, 36 37 AE 13, 65 37 AE 18, 89 91 m;f (N), 35 24 AE 15 59 VC (%) TPR: 68 AE 8 (N), 40 64 AE 11 (N), 24 64 AE 15, 24 61 AE 5, 40 61 AE 10 (N), 55 50 AE 25 (N), 31 48 AE 31 59 ; PPV: 65 AE 8, 40 61 AE 12 (N), 24 56 AE 10 (N), 55 56 AE 11 (N), 40 56 AE 16,…”

Section: Resultsmentioning

confidence: 99%

“…Parotid glands [22][23][24][26][27][28][29][30][31][32][34][35][36][37]40,42,[45][46][47][48][49][50][51][52][53][54][55][56][57][58]60,[63][64][65][66][68][69][70]72,73,[75][76][77][78]80,[82][83][84]86,87,90,91,93,95…”

Section: Organ At Riskunclassified

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Auto‐segmentation of organs at risk for head and neck radiotherapy planning: From atlas‐based to deep learning methods

et al. 2020

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Radiotherapy (RT) is one of the basic treatment modalities for cancer of the head and neck (H&N), which requires a precise spatial description of the target volumes and organs at risk (OARs) to deliver a highly conformal radiation dose to the tumor cells while sparing the healthy tissues. For this purpose, target volumes and OARs have to be delineated and segmented from medical images. As manual delineation is a tedious and time-consuming task subjected to intra/interobserver variability, computerized auto-segmentation has been developed as an alternative. The field of medical imaging and RT planning has experienced an increased interest in the past decade, with new emerging trends that shifted the field of H&N OAR auto-segmentation from atlas-based to deep learning-based approaches. In this review, we systematically analyzed 78 relevant publications on auto-segmentation of OARs in the H&N region from 2008 to date, and provided critical discussions and recommendations from various perspectives: image modalityboth computed tomography and magnetic resonance image modalities are being exploited, but the potential of the latter should be explored more in the future; OARthe spinal cord, brainstem, and major salivary glands are the most studied OARs, but additional experiments should be conducted for several less studied soft tissue structures; image databaseseveral image databases with the corresponding ground truth are currently available for methodology evaluation, but should be augmented with data from multiple observers and multiple institutions; methodologycurrent methods have shifted from atlas-based to deep learning autosegmentation, which is expected to become even more sophisticated; ground truthdelineation guidelines should be followed and participation of multiple experts from multiple institutions is recommended; performance metricsthe Dice coefficient as the standard volumetric overlap metrics should be accompanied with at least one distance metrics, and combined with clinical acceptability scores and risk assessments; segmentation performancethe best performing methods achieve clinically acceptable auto-segmentation for several OARs, however, the dosimetric impact should be also studied to provide clinically relevant endpoints for RT planning.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Auto‐segmentation of organs at risk for head and neck radiotherapy planning: From atlas‐based to deep learning methods

et al. 2020

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“…While some organs, such as the optic nerve, are systematically segmented during radiotherapy planning, annotation of other structures may be available only for a subset of patients. One solution to this problem is to independently train one model per class, as it was proposed in some recent deep learning works [28,22,33]. A limitation of this approach is, however, the need to perform time-consuming trainings for every class, while the number of classes of interest may be large.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Anatomically consistent CNN-based segmentation of organs-at-risk in cranial radiotherapy

et al. 2020

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Planning of radiotherapy involves accurate segmentation of a large number of organs at risk, i.e. organs for which irradiation doses should be minimized to avoid important side effects of the therapy. We propose a deep learning method for segmentation of organs at risk inside the brain region, from Magnetic Resonance (MR) images. Our system performs segmentation of eight structures: eye, lens, optic nerve, optic chiasm, pituitary gland, hippocampus, brainstem and brain. We propose an efficient algorithm to train neural networks for an end-to-end segmentation of multiple and nonexclusive classes, addressing problems related to computational costs and missing ground truth segmentations for a subset of classes. We enforce anatomical consistency of the result in a postprocessing step, in particular we introduce a graph-based algorithm for segmentation of the optic nerves, enforcing the connectivity between the eyes and the optic chiasm. We report cross-validated quantitative results on a database of 44 contrast-enhanced T1-weighted MRIs with provided segmentations of the considered organs at risk, which were originally used for radiotherapy planning. In addition, the segmentations produced by our model on an independent test set of 50 MRIs are evaluated by an experienced radiotherapist in order to qualitatively assess their accuracy. The mean distances between produced segmentations and the ground truth ranged from 0.1 mm to 0.7 mm across different organs. A vast majority (96 %) of the produced segmentations were found acceptable for radiotherapy planning.

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“…In recent years, owing to the development of deep convolutional neural networks (DCNNs), several automated methods have been proposed to address the problem of dose prediction [2][3][4][5] and organ segmentation [6][7][8][9] based on DCNNs. However, the previous studies either focused on organ segmentation or dose prediction, without considering them from a holistic perspective.…”

Section: Introductionmentioning

confidence: 99%

DeepEC: An error correction framework for dose prediction and organ segmentation using deep neural networks

Wang

Zhang

et al. 2020

Int J Intell Syst

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Radiotherapy is an indispensable part of adjuvant therapy for cancer that improves local control, overall survival, and the opportunity for good quality of life. Organ delineation and dose plan design are the key steps in the treatment. Organ delineation controls the area of radiotherapy and dose planning controls its intensity. However, both tasks are time-consuming, exhausting, and subjective, and automated methods are desirable. Although automated methods have been studied, the previous studies either focus on organ segmentation or dose prediction, without considering them from a holistic perspective. In this paper, we treat organ segmentation and dose prediction as similar tasks, and propose an error correction framework to improve their performance based on the same mechanism. The proposed error correction framework consists of a prediction network and a calibration network. The biggest difference between our framework and previous studies is that the state-of-the-art networks can be used as a prediction network or calibration network, and then the performance can be improved by the error correction mechanism. To evaluate the framework, we conducted a series of experiments on dose prediction and organ segmentation. These experimental results

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Technical Note: More accurate and efficient segmentation of organs‐at‐risk in radiotherapy with convolutional neural networks cascades

Cited by 53 publications

References 34 publications

Auto‐segmentation of organs at risk for head and neck radiotherapy planning: From atlas‐based to deep learning methods

Auto‐segmentation of organs at risk for head and neck radiotherapy planning: From atlas‐based to deep learning methods

Anatomically consistent CNN-based segmentation of organs-at-risk in cranial radiotherapy

DeepEC: An error correction framework for dose prediction and organ segmentation using deep neural networks

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