Clinically applicable deep learning framework for organs at risk delineation in CT images

Tang, Hao; Chen, Xuming; Liu, Yang; Lu, Zhipeng; You, Jinyuan; Yang, Mingzhou; Yao, Shengyu; Zhao, Guojun; Yi, Xu; Chen, Tingfeng; Liu, Yong; Xie, Xiaohui

doi:10.1038/s42256-019-0099-z

Cited by 142 publications

(154 citation statements)

References 61 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%

“…Submandibular glands DC (%) 91 m;f , 22 85 AE 10, 42 85, 60 84 AE 6, 24 83, 53 82 AE 5, 86 82 AE 5 (N), 40 82 AE 7, 30 82 AE 7, 50 81 AE 4, 46 81 AE 6 (N), 55 80 AE 7 (N), 24 80 AE 7, 26 80 AE 8 (•), 60 91 m;f , 77 91 m;f , 23 78 AE 7 (N), 60 78 AE 8 (N,IM), 66 77 AE 6, 34 75 AE 13 (N), 31 VC (%) TPR: 87 AE 5, 24 85 AE 6 (N), 55 80 AE 11, 50 79 AE 8 (N), 24 79 AE 9 (N), 40 72 AE 16 (N) 31 ; PPV: 85 AE 9 (N), 40 83 AE 11, 24 82 AE 9 (N), 24 82 AE 11 (N), 31 ASD (mm) ASSD: 1.2, 53 1.3 AE 1.2 26 ; ASD91 m;f : 91 m;f (N,IM) 66 ; ASD91 m;f : 0.9 AE 0.5 (N), 55 1.2 AE 0.7, 24 1.4 AE 1.0 (N), 40 2.0 AE 1.9 (N) 24 ; DTA91 m;f : 91 m;f , 77 1.2 AE 1.3, 42 1.9 AE 1.4 87 Brainstem DC (%) 93 AE 1, 97 93 AE 3, 27 92 AE 3, 40 92, 53 91 AE 1, 86 91 AE 3, 43 90 AE 1, 26 90 AE 2,…”

Section: Resultsmentioning

confidence: 99%

“…Although many TCIA databases include reference H&N OAR delineations, they are associated with considerable variability because of the lack of a standardized delineation protocol. As a result, some of them were augmented and/or combined into new publicly available databases, for example, the manual delineations of 28 OARs in 140 CT images from the Head-Neck Cetuximab and Head-Neck-PET-CT databases as well as in 175 CT images from an in-house database (https://github.com/ucicbcl/UaNet#Data), 30 the manual delineations of 21 OARs in 31 CT images from the Head-Neck Cetuximab and TCGA-HNSC databases forming the TCIA test & validation radiotherapy CT planning scan dataset (TCIA-RT) (https://github.com/deepmind/ tcia-ct-scan-dataset) database, 60 or the manual delineations of nine OARs in 48 CT images from the Head-Neck Cetuximab database forming the Public domain database for computational anatomy (PDDCA) (http://www.imagenglab.com/newsite/pddca/ ) database. 66 Examples of publicly available databases that do not originate from TCIA include the StructSeg (https://structseg2019.gra nd-challenge.org/Dataset/) database consisting of 50 CT images with 22 manually delineated OARs, and the MRI-RT (https://f igshare.com/s/a5e09113f5c07b3047df) database 105 consisting of 15 CT and 15 MR images of the same patients with 23 manually delineated OARs from the H&N region.…”

Section: Computed Tomography (Ct)mentioning

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

“…Maximum distance to agreement 27,77 max b2@B dðb; @AÞ HD 95 95-percentile Hausdorff distance 22,23,[29][30][31]35,[37][38][39][40]46,49,55,58,66,69…”

Section: Dta Maxmentioning

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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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%

Section: Computed Tomography (Ct)mentioning

confidence: 99%

Section: Organ At Riskunclassified

“…Maximum distance to agreement 27,77 max b2@B dðb; @AÞ HD 95 95-percentile Hausdorff distance 22,23,[29][30][31]35,[37][38][39][40]46,49,55,58,66,69…”

Section: Dta Maxmentioning

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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Three‐dimensional deep neural network for automatic delineation of cervical cancer in planning computed tomography images

Ding

Chen

Wang

et al. 2022

J Applied Clin Med Phys

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Purpose Radiation therapy is an essential treatment modality for cervical cancer, while accurate and efficient segmentation methods are needed to improve the workflow. In this study, a three‐dimensional V‐net model is proposed to automatically segment clinical target volume (CTV) and organs at risk (OARs), and to provide prospective guidance for low lose area. Material and methods A total of 130 CT datasets were included. Ninety cases were randomly selected as the training data, with 10 cases used as the validation data, and the remaining 30 cases as testing data. The V‐net model was implemented with Tensorflow package to segment the CTV and OARs, as well as regions of 5 Gy, 10 Gy, 15 Gy, and 20 Gy isodose lines covered. The auto‐segmentation by V‐net was compared to auto‐segmentation by U‐net. Four representative parameters were calculated to evaluate the accuracy of the delineation, including Dice similarity coefficients (DSC), Jaccard index (JI), average surface distance (ASD), and Hausdorff distance (HD). Results The V‐net and U‐net achieved the average DSC value for CTV of 0.85 and 0.83, average JI values of 0.77 and 0.75, average ASD values of 2.58 and 2.26, average HD of 11.2 and 10.08, respectively. As for the OARs, the performance of the V‐net model in the colon was significantly better than the U‐net model (p = 0.046), and the performance in the kidney, bladder, femoral head, and pelvic bones were comparable to the U‐net model. For prediction of low‐dose areas, the average DSC of the patients’ 5 Gy dose area in the test set were 0.88 and 0.83, for V‐net and U‐net, respectively. Conclusions It is feasible to use the V‐Net model to automatically segment cervical cancer CTV and OARs to achieve a more efficient radiotherapy workflow. In the delineation of most target areas and OARs, the performance of V‐net is better than U‐net. It also offers advantages with its feature of predicting the low‐dose area prospectively before radiation therapy (RT).

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A prior‐information‐based automatic segmentation method for the clinical target volume in adaptive radiotherapy of cervical cancer

Wang,

Chang,

Pei

et al. 2024

J Applied Clin Med Phys

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ObjectiveAdaptive planning to accommodate anatomic changes during treatment often requires repeated segmentation. In this study, prior patient‐specific data was integrateda into a registration‐guided multi‐channel multi‐path (Rg‐MCMP) segmentation framework to improve the accuracy of repeated clinical target volume (CTV) segmentation.MethodsThis study was based on CT image datasets for a total of 90 cervical cancer patients who received two courses of radiotherapy. A total of 15 patients were selected randomly as the test set. In the Rg‐MCMP segmentation framework, the first‐course CT images (CT1) were registered to second‐course CT images (CT2) to yield aligned CT images (aCT1), and the CTV in the first course (CTV1) was propagated to yield aligned CTV contours (aCTV1). Then, aCT1, aCTV1, and CT2 were combined as the inputs for 3D U‐Net consisting of a channel‐based multi‐path feature extraction network. The performance of the Rg‐MCMP segmentation framework was evaluated and compared with the single‐channel single‐path model (SCSP), the standalone registration methods, and the registration‐guided multi‐channel single‐path (Rg‐MCSP) model. The Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and average surface distance (ASD) were used as the metrics.ResultsThe average DSC of CTV for the deformable image DIR‐MCMP model was found to be 0.892, greater than that of the standalone DIR (0.856), SCSP (0.837), and DIR‐MCSP (0.877), which were improvements of 4.2%, 6.6%, and 1.7%, respectively. Similarly, the rigid body DIR‐MCMP model yielded an average DSC of 0.875, which exceeded standalone RB (0.787), SCSP (0.837), and registration‐guided multi‐channel single‐path (0.848), which were improvements of 11.2%, 4.5%, and 3.2%, respectively. These improvements in DSC were statistically significant (p < 0.05).ConclusionThe proposed Rg‐MCMP framework achieved excellent accuracy in CTV segmentation as part of the adaptive radiotherapy workflow.

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Clinically applicable deep learning framework for organs at risk delineation in CT images

Cited by 142 publications

References 61 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

Three‐dimensional deep neural network for automatic delineation of cervical cancer in planning computed tomography images

A prior‐information‐based automatic segmentation method for the clinical target volume in adaptive radiotherapy of cervical cancer

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