Dynamic multiatlas selection‐based consensus segmentation of head and neck structures from CT images

Haq, Rabia; Berry, Sean L.; Deasy, Joseph O.; Hunt, Margie A.; Veeraraghavan, Harini

doi:10.1002/mp.13854

Cited by 14 publications

(20 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…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%

“…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%

“…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%

“…Average surface distance, maximum 35,64,66,72,75,76,98,99 max P a2@A dða;@BÞ j@Aj ; P b2@B dðb;@AÞ j@Bj…”

Section: Asd Maxmentioning

confidence: 99%

See 4 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: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

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%

“…Average surface distance, maximum 35,64,66,72,75,76,98,99 max P a2@A dða;@BÞ j@Aj ; P b2@B dðb;@AÞ j@Bj…”

Section: Asd Maxmentioning

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

show abstract

Head and neck cancer patient images for determining auto‐segmentation accuracy in T2‐weighted magnetic resonance imaging through expert manual segmentations

et al. 2020

Self Cite

View full text Add to dashboard Cite

Purpose The use of magnetic resonance imaging (MRI) in radiotherapy treatment planning has rapidly increased due to its ability to evaluate patient’s anatomy without the use of ionizing radiation and due to its high soft tissue contrast. For these reasons, MRI has become the modality of choice for longitudinal and adaptive treatment studies. Automatic segmentation could offer many benefits for these studies. In this work, we describe a T2‐weighted MRI dataset of head and neck cancer patients that can be used to evaluate the accuracy of head and neck normal tissue auto‐segmentation systems through comparisons to available expert manual segmentations. Acquisition and validation methods T2‐weighted MRI images were acquired for 55 head and neck cancer patients. These scans were collected after radiotherapy computed tomography (CT) simulation scans using a thermoplastic mask to replicate patient treatment position. All scans were acquired on a single 1.5 T Siemens MAGNETOM Aera MRI with two large four‐channel flex phased‐array coils. The scans covered the region encompassing the nasopharynx region cranially and supraclavicular lymph node region caudally, when possible, in the superior–inferior direction. Manual contours were created for the left/right submandibular gland, left/right parotids, left/right lymph node level II, and left/right lymph node level III. These contours underwent quality assurance to ensure adherence to predefined guidelines, and were corrected if edits were necessary. Data format and usage notes The T2‐weighted images and RTSTRUCT files are available in DICOM format. The regions of interest are named based on AAPM’s Task Group 263 nomenclature recommendations (Glnd_Submand_L, Glnd_Submand_R, LN_Neck_II_L, Parotid_L, Parotid_R, LN_Neck_II_R, LN_Neck_III_L, LN_Neck_III_R). This dataset is available on The Cancer Imaging Archive (TCIA) by the National Cancer Institute under the collection “AAPM RT‐MAC Grand Challenge 2019” (https://doi.org/10.7937/tcia.2019.bcfjqfqb). Potential applications This dataset provides head and neck patient MRI scans to evaluate auto‐segmentation systems on T2‐weighted images. Additional anatomies could be provided at a later time to enhance the existing library of contours.

show abstract

Head and neck cancer patient images for determining auto‐segmentation accuracy in T2‐weighted magnetic resonance imaging through expert manual segmentations

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

Dynamic multiatlas selection‐based consensus segmentation of head and neck structures from CT images

Cited by 14 publications

References 32 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

Head and neck cancer patient images for determining auto‐segmentation accuracy in T2‐weighted magnetic resonance imaging through expert manual segmentations

Head and neck cancer patient images for determining auto‐segmentation accuracy in T2‐weighted magnetic resonance imaging through expert manual segmentations

Contact Info

Product

Resources

About