Deep learning vs. atlas-based models for fast auto-segmentation of the masticatory muscles on head and neck CT images

Chen, Wen; Li, Yimin; Dyer, Brandon A.; Xue, Fu‐Shan; Rao, Shyam; Benedict, Stanley H; Chen, Quan; Rong, Yi

doi:10.21203/rs.3.rs-23941/v2

Cited by 9 publications

(9 citation statements)

References 24 publications

(36 reference statements)

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“…Assessing inter‐observer variability is always challenging, because of the difficulties in defining references. This problem can be addressed in different ways, for example, by performing two‐by‐two comparisons 20 , 21 , 22 or by designating one set of contours as the reference. 19 , 23 In this study, we applied the STAPLE finalization algorithm 24 to the contours outlined by all ROs, to define a reference set (Ref) used as the shared gold standard.…”

Section: Methodsmentioning

confidence: 99%

Clinical validation of an automatic atlas‐based segmentation tool for male pelvis CT images

Casati

Piffer

Calusi

et al. 2022

J Applied Clin Med Phys

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This retrospective work aims to evaluate the possible impact on intraand inter-observer variability, contouring time, and contour accuracy of introducing a pelvis computed tomography (CT) auto-segmentation tool in radiotherapy planning workflow. Methods: Tests were carried out on five structures (bladder, rectum, pelvic lymph-nodes, and femoral heads) of six previously treated subjects, enrolling five radiation oncologists (ROs) to manually re-contour and edit auto-contours generated with a male pelvis CT atlas created with the commercial software MIM MAESTRO. The ROs first delineated manual contours (M). Then they modified the auto-contours, producing automatic-modified (AM) contours. The procedure was repeated to evaluate intra-observer variability, producing M1, M2, AM1, and AM2 contour sets (each comprising 5 structures × 6 test patients × 5 ROs = 150 contours), for a total of 600 contours. Potential time savings was evaluated by comparing contouring and editing times. Structure contours were compared to a reference standard by means of Dice similarity coefficient (DSC) and mean distance to agreement (MDA), to assess intra-and inter-observer variability. To exclude any automation bias, ROs evaluated both M and AM sets as "clinically acceptable" or "to be corrected" in a blind test. Results: Comparing AM to M sets, a significant reduction of both inter-observer variability (p < 0.001) and contouring time (-45% whole pelvis, p < 0.001) was obtained. Intra-observer variability reduction was significant only for bladder and femoral heads (p < 0.001). The statistical test showed no significant bias. Conclusion: Our atlas-based workflow proved to be effective for clinical practice as it can improve contour reproducibility and generate time savings. Based on these findings, institutions are encouraged to implement their autosegmentation method.

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Section: Methodsmentioning

confidence: 99%

Clinical validation of an automatic atlas‐based segmentation tool for male pelvis CT images

Casati

Piffer

Calusi

et al. 2022

J Applied Clin Med Phys

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“…In particular, convolutional neural networks (CNN) have shown encouraging results in human organ segmentation. Several studies have demonstrated that deep learning-based segmentation yielded more consistent and more accurate results than atlas-based methods for clinical images 21 , 22 . It also outperformed the atlas-based methods in terms of speed 23 .…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based segmentation of the thorax in mouse micro-CT scans

Malimban

Lathouwers

Qian

et al. 2022

Sci Rep

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For image-guided small animal irradiations, the whole workflow of imaging, organ contouring, irradiation planning, and delivery is typically performed in a single session requiring continuous administration of anaesthetic agents. Automating contouring leads to a faster workflow, which limits exposure to anaesthesia and thereby, reducing its impact on experimental results and on animal wellbeing. Here, we trained the 2D and 3D U-Net architectures of no-new-Net (nnU-Net) for autocontouring of the thorax in mouse micro-CT images. We trained the models only on native CTs and evaluated their performance using an independent testing dataset (i.e., native CTs not included in the training and validation). Unlike previous studies, we also tested the model performance on an external dataset (i.e., contrast-enhanced CTs) to see how well they predict on CTs completely different from what they were trained on. We also assessed the interobserver variability using the generalized conformity index ($$\hbox {CI}_{\mathrm{gen}}$$ CI gen ) among three observers, providing a stronger human baseline for evaluating automated contours than previous studies. Lastly, we showed the benefit on the contouring time compared to manual contouring. The results show that 3D models of nnU-Net achieve superior segmentation accuracy and are more robust to unseen data than 2D models. For all target organs, the mean surface distance (MSD) and the Hausdorff distance (95p HD) of the best performing model for this task (nnU-Net 3d_fullres) are within 0.16 mm and 0.60 mm, respectively. These values are below the minimum required contouring accuracy of 1 mm for small animal irradiations, and improve significantly upon state-of-the-art 2D U-Net-based AIMOS method. Moreover, the conformity indices of the 3d_fullres model also compare favourably to the interobserver variability for all target organs, whereas the 2D models perform poorly in this regard. Importantly, the 3d_fullres model offers 98% reduction in contouring time.

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“…However, atlas‐based auto‐segmentation uses a fixed size. Hence, this limits its ability to adapt to the difference in patient anatomy 9 …”

Section: Introductionmentioning

confidence: 99%

“…Hence, this limits its ability to adapt to the difference in patient anatomy. 9 Artificial intelligence (AI)-based methods have recently been proposed for the segmentation required for treatment planning. AI-based algorithms can perform highly intensive computations.…”

Section: Introductionmentioning

confidence: 99%

Deep learning‐based auto segmentation using generative adversarial network on magnetic resonance images obtained for head and neck cancer patients

Kawahara

Tsuneda

Ozawa³

et al. 2022

J Applied Clin Med Phys

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Purpose Adaptive radiotherapy requires auto‐segmentation in patients with head and neck (HN) cancer. In the current study, we propose an auto‐segmentation model using a generative adversarial network (GAN) on magnetic resonance (MR) images of HN cancer for MR‐guided radiotherapy (MRgRT). Material and methods In the current study, we used a dataset from the American Association of Physicists in Medicine MRI Auto‐Contouring (RT‐MAC) Grand Challenge 2019. Specifically, eight structures in the MR images of HN region, namely submandibular glands, lymph node level II and level III, and parotid glands, were segmented with the deep learning models using a GAN and a fully convolutional network with a U‐net. These images were compared with the clinically used atlas‐based segmentation. Results The mean Dice similarity coefficient (DSC) of the U‐net and GAN models was significantly higher than that of the atlas‐based method for all the structures (p < 0.05). Specifically, the maximum Hausdorff distance (HD) was significantly lower than that in the atlas method (p < 0.05). Comparing the 2.5D and 3D U‐nets, the 3D U‐net was superior in segmenting the organs at risk (OAR) for HN patients. The DSC was highest for 0.75–0.85, and the HD was lowest within 5.4 mm of the 2.5D GAN model in all the OARs. Conclusions In the current study, we investigated the auto‐segmentation of the OAR for HN patients using U‐net and GAN models on MR images. Our proposed model is potentially valuable for improving the efficiency of HN RT treatment planning.

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Deep learning vs. atlas-based models for fast auto-segmentation of the masticatory muscles on head and neck CT images

Cited by 9 publications

References 24 publications

Clinical validation of an automatic atlas‐based segmentation tool for male pelvis CT images

Clinical validation of an automatic atlas‐based segmentation tool for male pelvis CT images

Deep learning-based segmentation of the thorax in mouse micro-CT scans

Deep learning‐based auto segmentation using generative adversarial network on magnetic resonance images obtained for head and neck cancer patients

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