The feasibility of atlas‐based automatic segmentation of MRI for H&amp;N radiotherapy planning

Wardman, Kieran; Prestwich, R.; Gooding, Mark J.; Speight, R.

doi:10.1120/jacmp.v17i4.6051

Cited by 27 publications

(41 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Atlas 27,29,34,44,52,58,59,61,68,69,71,73,[78][79][80][81]84,85,87,[89][90][91]93,94,99 with shape/appearance models 38,66,76,77,82,86,92,95 with intensity models [97][98][99] with feature classification 35,63,72,75,83,86 with contour refinement 72,76,92 with level set refinement 91 Feature classification 64,74 Localization model and feature classification 51,56 Level-set statistical model 88,…”

Section: Methodsmentioning

confidence: 99%

“…While CT images provide a good visibility of the bony anatomy, the contrast differences between various soft tissues are relatively low, and can be to a certain degree improved by using an intravenous contrast enhancement agent. 68,84,95,98,99 On the other hand, MR imaging gained a broad adoption because of its superior soft tissue contrast resolution compared to CT images and various imaging setups. In the recent consensus for CT-based manual delineation guidelines for OARs in the H&N region, 102 it is strongly recommended to use, besides CT, also MR images to facilitate the delineation of several soft tissue OARs.…”

Section: A Image Modalitymentioning

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

“…The most established consensus 102 encompasses a complete set of OARs in the H&N region, with the expert recommendation to always include the parotid glands, submandibular glands, spinal cord, and pharyngeal constrictor muscles in the RT plan. Other guidelines are focused on OARs involved in the nasopharyngeal carcinoma (i.e., the temporal lobe, parotid glands, spinal cord, and inner and middle ear), 122 swallowing (i.e., the pharyngeal constrictor muscles, cricopharyngeal muscle, esophagus inlet muscles, cervical esophagus, base of tongue, and larynx), 124 salivary functioning (i.e., the parotid glands, submandibular glands, sublingual gland, and minor salivary glands in the soft palate, lips, and cheeks), 125 hearing and balance (i.e., the inner and middle ear), 126 brachial plexopathy (i.e., the brachial plexus and 5:L, 83 7, 98 7, 38 10, 77 10:L, 95 10:L, 78 10:L 87 11À18 11:L, 97 12:L, 58 12, 67 13, 93 14:L, 68 14:L, 29 5|10, 74 15:5F, 28 8|8,16:L, 72 16, 90 18:L, 76 18:L, 64 18:L 99 20À25 20:L, 85 y 20, 89 20, 84 20, 59 20, 27 21:L, 61 14|10, 88 52 40, 80 41, 96 41, 25 42, 75 44:5F, 57 45:L,32N, 35 33|15N, 31,54 32+6|10N, 24 50:5F, 65 Legend: nnumber of cases with a model or without a training set; m|nm cases for training, n cases for testing; m+k|nm cases for training (if omitted, models are used), k cases for model selection, n cases for testing; n:kFn cases with the k-fold cross-validation; n:Ln cases with the leave-one-out validation; *for 30 patients, 2 or more images available, together 36|262; Nevaluated on the PDDCA database; 66...…”

Section: E Ground Truthmentioning

confidence: 99%

See 3 more Smart Citations

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

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: A Image Modalitymentioning

confidence: 99%

Section: Organ At Riskunclassified

Section: E Ground Truthmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Such problems have been addressed in clinical applications using "atlas-based" automated segmentation software. [1][2][3] Despite the popularity of such software, the recent deep learning revolution, especially the fully convolutional neural networks (CNN), [4][5][6][7][8] has turned the tables due to its significant improvement in terms of segmentation accuracy, consistency, and efficiency. Lustberg et al 9 and Lavdas et al 10 demonstrated that CNN contouring demonstrated promising results in CT and MR image segmentation as compared with atlas-based methods.…”

mentioning

confidence: 99%

A study of positioning orientation effect on segmentation accuracy using convolutional neural networks for rectal cancer

Men

Boimel

Janopaul‐Naylor

et al. 2018

J Applied Clin Med Phys

View full text Add to dashboard Cite

PurposeConvolutional neural networks (CNN) have greatly improved medical image segmentation. A robust model requires training data can represent the entire dataset. One of the differing characteristics comes from variability in patient positioning (prone or supine) for radiotherapy. In this study, we investigated the effect of position orientation on segmentation using CNN.MethodsData of 100 patients (50 in supine and 50 in prone) with rectal cancer were collected for this study. We designed three sets of experiments for comparison: (a) segmentation using the model trained with data from the same orientation; (b) segmentation using the model trained with data from the opposite orientation; (c) segmentation using the model trained with data from both orientations. We performed fivefold cross‐validation. The performance was evaluated on segmentation of the clinical target volume (CTV), bladder, and femurs with Dice similarity coefficient (DSC) and Hausdorff distance (HD).ResultsCompared with models trained on cases positioned in the same orientation, the models trained with cases positioned in the opposite orientation performed significantly worse (P < 0.05) on CTV and bladder segmentation, but had comparable accuracy for femurs (P > 0.05). The average DSC values were 0.74 vs 0.84, 0.85 vs 0.88, and 0.91 vs 0.91 for CTV, bladder, and femurs, respectively. The corresponding HD values (mm) were 16.6 vs 14.6, 8.4 vs 8.1, and 6.3 vs 6.3, respectively. The models trained with data from both orientations have comparable accuracy (P > 0.05), with average DSC of 0.84, 0.88, and 0.91 and HD of 14.4, 8.1, and 6.3, respectively.ConclusionsOrientation affects the accuracy for CTV and bladder, but has negligible effect on the femurs. The model trained from data combining both orientations performs as well as a model trained with data from the same orientation for all the organs. These observations can offer guidance on the choice of training data for accurate segmentation.

show abstract

Automatic segmentation and applicator reconstruction for CT‐based brachytherapy of cervical cancer using 3D convolutional neural networks

Zhang

Yang

Jiang

et al. 2020

J Applied Clin Med Phys

View full text Add to dashboard Cite

In this study, we present deep learning‐based approaches to automatic segmentation and applicator reconstruction with high accuracy and efficiency in the planning computed tomography (CT) for cervical cancer brachytherapy (BT). A novel three‐dimensional (3D) convolutional neural network (CNN) architecture was proposed and referred to as DSD‐UNET. The dataset of 91 patients received CT‐based BT of cervical cancer was used to train and test DSD‐UNET model for auto‐segmentation of high‐risk clinical target volume (HR‐CTV) and organs at risk (OARs). Automatic applicator reconstruction was achieved with DSD‐UNET‐based segmentation of applicator components followed by 3D skeletonization and polynomial curve fitting. Digitization of the channel paths for tandem and ovoid applicator in the planning CT was evaluated utilizing the data from 32 patients. Dice similarity coefficient (DSC), Jaccard Index (JI), and Hausdorff distance (HD) were used to quantitatively evaluate the accuracy. The segmentation performance of DSD‐UNET was compared with that of 3D U‐Net. Results showed that DSD‐UNET method outperformed 3D U‐Net on segmentations of all the structures. The mean DSC values of DSD‐UNET method were 86.9%, 82.9%, and 82.1% for bladder, HR‐CTV, and rectum, respectively. For the performance of automatic applicator reconstruction, outstanding segmentation accuracy was first achieved for the intrauterine and ovoid tubes (average DSC value of 92.1%, average HD value of 2.3 mm). Finally, HDs between the channel paths determined automatically and manually were 0.88 ± 0.12 mm, 0.95 ± 0.16 mm, and 0.96 ± 0.15 mm for the intrauterine, left ovoid, and right ovoid tubes, respectively. The proposed DSD‐UNET method outperformed the 3D U‐Net and could segment HR‐CTV, bladder, and rectum with relatively good accuracy. Accurate digitization of the channel paths could be achieved with the DSD‐UNET‐based method. The proposed approaches could be useful to improve the efficiency and consistency of treatment planning for cervical cancer BT.

show abstract

The feasibility of atlas‐based automatic segmentation of MRI for H&N radiotherapy planning

Cited by 27 publications

References 29 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

A study of positioning orientation effect on segmentation accuracy using convolutional neural networks for rectal cancer

Automatic segmentation and applicator reconstruction for CT‐based brachytherapy of cervical cancer using 3D convolutional neural networks

Contact Info

Product

Resources

About