Multi-organ segmentation of the head and neck area: an efficient hierarchical neural networks approach

Tappeiner, Elias; Pröll, Samuel; Hönig, Markus; Raudaschl, Patrik; Zaffino, Paolo; Spadea, Maria Francesca; Sharp, G; Schubert, Rainer; Fritscher, Karl D.

doi:10.1007/s11548-019-01922-4

Cited by 48 publications

(38 citation statements)

References 20 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%

“…After reviewing their abstracts, 49 were considered to be relevant and were further supplemented with selected publications from their list of references. In total, we collected 75 publications 22–96 focused on RT planning and three studies focused on hyperthermia therapy planning 97–99 from 2008 to date (Fig. 1), along with three review papers related to auto‐segmentation in the H&N region 19–21 .…”

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

“…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: 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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“…In addition to the collection of molecular and clinical data, TCGA has collected WSI data from a subset of its participants . Some other examples of public digital slide datasets are the breast cancer images used for the CAMELYON competition ; the Medical Image Computing and Computer Assisted Intervention Society (MICCAI) 2014 brain tumor digital pathology challenge for distinguishing brain cancer subtypes ; and the Tumor Proliferation Assessment Challenge (TUPAC16), which includes hundreds of cases . The Grand Challenge website (https://grand-challenge.org/challenges/) maintains a list of all challenges that have been organized in the field of medical image analysis.…”

Section: Hurdles and Solutions For Implementing Computational Pathologymentioning

confidence: 99%

Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association

Abels

Pantanowitz

Aeffner

et al. 2019

The Journal of Pathology

330

287

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In this white paper, experts from the Digital Pathology Association (DPA) define terminology and concepts in the emerging field of computational pathology, with a focus on its application to histology images analyzed together with their associated patient data to extract information. This review offers a historical perspective and describes the potential clinical benefits from research and applications in this field, as well as significant obstacles to adoption. Best practices for implementing computational pathology workflows are presented. These include infrastructure considerations, acquisition of training data, quality assessments, as well as regulatory, ethical, and cyber‐security concerns. Recommendations are provided for regulators, vendors, and computational pathology practitioners in order to facilitate progress in the field. © 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

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Cascaded deep learning‐based auto‐segmentation for head and neck cancer patients: Organs at risk on T2‐weighted magnetic resonance imaging

Korte

Hardcastle

et al. 2021

Medical Physics

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To investigate multiple deep learning methods for automated segmentation (auto-segmentation) of the parotid glands, submandibular glands, and level II and level III lymph nodes on magnetic resonance imaging (MRI).Outlining radiosensitive organs on images used to assist radiation therapy (radiotherapy) of patients with head and neck cancer (HNC) is a time-consuming task, in which variability between observers may directly impact on patient treatment outcomes. Auto-segmentation on computed tomography imaging has been shown to result in significant time reductions and more consistent outlines of the organs at risk. Methods: Three convolutional neural network (CNN)-based auto-segmentation architectures were developed using manual segmentations and T2-weighted MRI images provided from the American Association of Physicists in Medicine (AAPM) radiotherapy MRI auto-contouring (RT-MAC) challenge dataset (n = 31). Auto-segmentation performance was evaluated with segmentation similarity and surface distance metrics on the RT-MAC dataset with institutional manual segmentations (n = 10). The generalizability of the autosegmentation methods was assessed on an institutional MRI dataset (n = 10). Results: Auto-segmentation performance on the RT-MAC images with institutional segmentations was higher than previously reported MRI methods for the parotid glands (Dice: 0.860 ± 0.067, mean surface distance [MSD]: 1.33 ± 0.40 mm) and the first report of MRI performance for submandibular glands (Dice: 0.830 ± 0.032, MSD: 1.16 ± 0.47 mm). We demonstrate that highresolution auto-segmentations with improved geometric accuracy can be generated for the parotid and submandibular glands by cascading a localizer CNN and a cropped high-resolution CNN. Improved MSDs were observed between automatic and manual segmentations of the submandibular glands when a lowresolution auto-segmentation was used as prior knowledge in the second-stage CNN. Reduced auto-segmentation performance was observed on our institutional MRI dataset when trained on external RT-MAC images; only the parotid gland auto-segmentations were considered clinically feasible for manual correction (Dice: 0.775 ± 0.105, MSD: 1.20 ± 0.60 mm). Conclusions: This work demonstrates that CNNs are a suitable method to auto-segment the parotid and submandibular glands on MRI images of patients with HNC, and that cascaded CNNs can generate high-resolution segmentations with improved geometric accuracy.Deep learning methods may be suitable for auto-segmentation of the parotid glands on T2-weighted MRI images from different scanners, but further work is required to improve the performance and

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Multi-organ segmentation of the head and neck area: an efficient hierarchical neural networks approach

Cited by 48 publications

References 20 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

Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association

Cascaded deep learning‐based auto‐segmentation for head and neck cancer patients: Organs at risk on T2‐weighted magnetic resonance imaging

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