A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning

Agn, Mikael; Rosenschöld, Per Munck af; Puonti, Oula; Lundemann, Michael; Mancini, Laura; Papadaki, Anastasia; Thust, Steffi; Ashburner, John; Law, Ian; Leemput, Koen Van

doi:10.1016/j.media.2019.03.005

Cited by 35 publications

(29 citation statements)

References 76 publications

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“…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: Methodsologymentioning

confidence: 99%

“…The start of this trend is already indicated by the recent increase in the number of studies that include the MR image modality. 38,40,43,[57][58][59] In a single study where OARs were independently auto-segmented from CT and MR images of the same patients, the results for MR images outperformed those for CT images in the case of the parotid glands, eyeballs, and brainstem. 68 Although methods for MR-only RT planning are being developed, 135 their routine clinical implementation is still very limited, as challenges remain of how to assign data on electron density to MR images for the purpose of dose calculation 133 by means of synthetic CT image generation 136 or MR-to-CT image registration.…”

Section: A Image Modalitymentioning

confidence: 99%

“…Conventional CT [22][23][24][26][27][28][30][31][32][33][34][35][36][37][38][39][40][41][42][44][45][46][47][48][49][50][51][52][53][54][55][56][57][59][60][61][62][64][65][66][67][68][69][70]72,73,[76][77][78][79][80][81][82]…”

Section: Computed Tomography (Ct)unclassified

“…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%

“…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: Methodsologymentioning

confidence: 99%

Section: A Image Modalitymentioning

confidence: 99%

Section: Computed Tomography (Ct)unclassified

Section: E Ground Truthmentioning

confidence: 99%

“…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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SOMA: Subject‐, object‐, and modality‐adapted precision atlas approach for automatic anatomy recognition and delineation in medical images

Udupa

Odhner

et al. 2021

Medical Physics

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In the multi-atlas segmentation (MAS) method, a large enough atlas set, which can cover the complete spectrum of the whole population pattern of the target object will benefit the segmentation quality. However, the difficulty in obtaining and generating such a large set of atlases and the computational burden required in the segmentation procedure make this approach impractical. In this paper, we propose a method called SOMA to select subject-, object-, and modality-adapted precision atlases for automatic anatomy recognition in medical images with pathology, following the idea that different regions of the target object in a novel image can be recognized by different atlases with regionally best similarity, so that effective atlases have no need to be globally similar to the target subject and also have no need to be overall similar to the target object. Methods: The SOMA method consists of three main components: atlas building, object recognition, and object delineation. Considering the computational complexity, we utilize an all-to-template strategy to align all images to the same image space belonging to the root image determined by the minimum spanning tree (MST) strategy among a subset of radiologically near-normal images. The object recognition process is composed of two stages: rough recognition and refined recognition. In rough recognition, subimage matching is conducted between the test image and each image of the whole atlas set, and only the atlas corresponding to the best-matched subimage contributes to the recognition map regionally. The frequency of best match for each atlas is recorded by a counter, and the atlases with the highest frequencies are selected as the precision atlases. In refined recognition, only the precision atlases are examined, and the subimage matching is conducted in a nonlocal manner of searching to further increase the accuracy of boundary matching. Delineation is based on a U-net-based deep learning network, where the original gray scale image together with the fuzzy map from refined recognition compose a two-channel input to the network, and the output is a segmentation map of the target object. Results: Experiments are conducted on computed tomography (CT) images with different qualities in two body regions -head and neck (H&N) and thorax, from 298 subjects with nine objects and 241 subjects with six objects, respectively. Most objects achieve a localization error within two voxels after refined recognition, with marked improvement in localization accuracy from rough to refined recognition of 0.6-3 mm in H&N and 0.8-4.9 mm in thorax, and also in delineation accuracy (Dice coefficient) from refined recognition to delineation of 0.01-0.11 in H&N and 0.01-0.18 in thorax. Conclusions:The SOMA method shows high accuracy and robustness in anatomy recognition and delineation. The improvements from rough to refined 7806

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Learning Tumor-Induced Deformations to Improve Tumor-Bearing Brain MR Segmentation

Meng

Kyan

2022

Lecture Notes in Computer Science

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A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning

Cited by 35 publications

References 76 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

SOMA: Subject‐, object‐, and modality‐adapted precision atlas approach for automatic anatomy recognition and delineation in medical images

Learning Tumor-Induced Deformations to Improve Tumor-Bearing Brain MR Segmentation

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