Deep Learning-Based Delineation of Head and Neck Organs at Risk: Geometric and Dosimetric Evaluation

Rooij, Ward van; Dahele, Max; Brandao, Hugo Ribeiro; Delaney, Alexander R.; Slotman, B.J.; Verbakel, Wilko F.A.R.

doi:10.1016/j.ijrobp.2019.02.040

Cited by 90 publications

(104 citation statements)

References 19 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%

“…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

“…91 In 2015 § , six different teams participated in a challenge to segment the brainstem, mandible, optic chiasm, optic nerves, parotid glands, and ASD (mm) ASSD: 1.4 AE 0.4, 32 1.8 AE 0.4 (supraglottic) 32 ; DTA91 m;f : 91 m;f (o = 5,p = 10,S) 77 Trachea DC (%) 91 m;f (o = 2,p = 12) 63 Cochlea DC (%) 78 AE 8 (o = 2,p = 24,•), 60 76 AE 9 (o = 2,p = 8), 60 91 m;f (o = 5,p = 10,S), 77 50 AE 13, 32 Table VII does not report comparisons to the ground truth that was obtained by manually corrected or merged auto-segmentation results. 32,80,96 In the case the results were reported separately for multiple versions of a method Variations of the volumetric coefficient (VC) TPR Sensitivity 24,31,40,41,50,55,56,59,67,68,71,90,93,94,96 jA\Bj jAj TNR Specificity 41,93,94,96 jðA[BÞ C j jA C j PPV Positive predictive value (inclusion) 24,31,40,55,56,68 jA\Bj jBj FDR False discovery rate (segmented volume) 50,59 jBnAj jBj…”

Section: G Segmentation Performancementioning

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: Organ At Riskunclassified

Section: G Segmentation Performancementioning

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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“…Many studies have shown that although it is important to quantify the degree of variation or uncertainty of the contouring, it is more important to determine the dose difference and clinical impact [10,11,14,16,17,22,23]. In earlier work, van Rooij et al [17] studied the accuracy of automatic delineation of organs at risk in the head and neck region based on deep learning techniques while using geometric indices and dosimetric indices, and they analyzed the correlation between the geometric index SDC (mean value of the DSC) and dose difference. That study found that there was a weak correlation between the SDC and ΔD for all of the OARs through automatic segmentation, r = -0.24, P = 0.002, but the correlation was not speci c to a certain OAR or a certain patient.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, there is considerable variability between them, and different auto-segmentation studies use different geometric indices to evaluate the contouring results; further, different indices have different properties [12][13][14]. Under the assumption of a reference contour, the method for clinically assessing the accuracy of radiotherapy (RT) contours is to determine and predict the deviation of its dosimetric indices based on the dose distribution of the radiation treatment plan [10,[15][16][17]. The relationships between geometric indices and dosimetric indices are yet to be further studied.…”

Section: Introductionmentioning

confidence: 99%

Clinically oriented contour evaluation using geometric and dosimetric indices based on simple geometric transformations

Xian

Xiao

et al. 2020

Preprint

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Abstract Background: In radiotherapy, geometric indices are often used to evaluate the accuracy of contouring; however, the ability of geometric indices to identify the error of contouring results is limited, and they do not consider any clinical background. Based on the reference contouring, we systematically introduced the known geometric errors to study the relationship between geometric indices and dosimetric indices and evaluated the clinical feasibility of assessing the accuracy of contouring based on geometric indices alone.Materials and Methods: A C-shaped target, organ at risk (Core), and intensity-modulated radiotherapy (IMRT) plan outlined in the American Association of Physicists in Medicine (AAPM) TG-119 report (The report of Task Group 119 of the AAPM) were used as references. Translation, scaling, rotation (except for the Core), and sine function transformation were performed to simulate the test contours. The corresponding dosimetric indices were obtained from the original dose distribution of the radiotherapy plan, and correlations (R²) between geometric and dosimetric indices were quantified through linear regression. The Wilcoxon signed rank test was used to compare the differences between the geometric indices of three different directions of translation transformations.Results: The correlations between the geometric and dosimetric indices were inconsistent for the contouring of the target and Core after the geometric transformation. Except for the sine function transformation (R²: 0.04–0.023, P > 0.05), the other geometric transformations of the planning target volume (PTV) had correlations with the dosimetric indices D98% and Dmean (R²: 0.689–0.988), 80% of which were strongly correlated. The correlation results for the other geometric transformations in the Core were similar to those in the PTV except for the posterior direction transformation. The results of Wilcoxon signed rank test showed that only the P-values of volumetric geometric indices of PTV were less than 0.05.Conclusions: The dosimetric indices are heavily influenced by the contour differences, thus highlighting their importance in the evaluation process. Clinically, an assessment of the contour accuracy of the region of interest is not feasible based on geometric indices alone, and should be combined with dosimetric indices.

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Geometric and dosimetric evaluation of deep learning based auto‐segmentation for clinical target volume on breast cancer

Yang

Guo

Fang

et al. 2023

J Applied Clin Med Phys

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Background: Recently, target auto-segmentation techniques based on deep learning (DL) have shown promising results. However, inaccurate target delineation will directly affect the treatment planning dose distribution and the effect of subsequent radiotherapy work. Evaluation based on geometric metrics alone may not be sufficient for target delineation accuracy assessment. The purpose of this paper is to validate the performance of automatic segmentation with dosimetric metrics and try to construct new evaluation geometric metrics to comprehensively understand the dose-response relationship from the perspective of clinical application. Materials and Methods: A DL-based target segmentation model was developed by using 186 manual delineation modified radical mastectomy breast cancer cases. The resulting DL model were used to generate alternative target contours in a new set of 48 patients. The Auto-plan was reoptimized to ensure the same optimized parameters as the reference Manual-plan. To assess the dosimetric impact of target auto-segmentation,not only common geometric metrics but also new spatial parameters with distance and relative volume (R V ) to target were used. Correlations were performed using Spearman's correlation between segmentation evaluation metrics and dosimetric changes. Results: Only strong (|R 2 | > 0.6, p < 0.01) or moderate (|R 2 | > 0.4, p < 0.01) Pearson correlation was established between the traditional geometric metric and three dosimetric evaluation indices to target (conformity index,homogeneity index, and mean dose). For organs at risk (OARs), inferior or no significant relationship was found between geometric parameters and dosimetric differences. Furthermore, we found that OARs dose distribution was affected by boundary error of target segmentation instead of distance and R V to target. Conclusions: Current geometric metrics could reflect a certain degree of dose effect of target variation. To find target contour variations that do lead to OARs dosimetry changes, clinically oriented metrics that more accurately reflect how segmentation quality affects dosimetry should be constructed.

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Deep Learning-Based Delineation of Head and Neck Organs at Risk: Geometric and Dosimetric Evaluation

Cited by 90 publications

References 19 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

Clinically oriented contour evaluation using geometric and dosimetric indices based on simple geometric transformations

Geometric and dosimetric evaluation of deep learning based auto‐segmentation for clinical target volume on breast cancer

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