Feasibility of Continual Deep Learning-Based Segmentation for Personalized Adaptive Radiation Therapy in Head and Neck Area

Kim, Nalee; Chun, Jaehee; Chang, Jee Suk; Lee, Chang Geol; Keum, Ki Chang; Kim, Jin Sung

doi:10.3390/cancers13040702

Cited by 23 publications

(27 citation statements)

References 50 publications

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“…The DLBAS proved to be robust and reliable in automatic segmentation as the results were very similar to the ground truth. The mean DSC and HD values of this study are similar to those recorded in previous human studies (DSC = 0.79 and HD = 3.18 mm) ( 31 ). In the case of OARs with high accuracy, the boundaries were distinctly common and the variation among the test sets was small.…”

Section: Discussionsupporting

confidence: 90%

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Deep-Learning-Based Automatic Segmentation of Head and Neck Organs for Radiation Therapy in Dogs

Park

Choi

et al. 2021

Front. Vet. Sci.

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Purpose: This study was conducted to develop a deep learning-based automatic segmentation (DLBAS) model of head and neck organs for radiotherapy (RT) in dogs, and to evaluate the feasibility for delineating the RT planning.Materials and Methods: The segmentation indicated that there were potentially 15 organs at risk (OARs) in the head and neck of dogs. Post-contrast computed tomography (CT) was performed in 90 dogs. The training and validation sets comprised 80 CT data sets, including 20 test sets. The accuracy of the segmentation was assessed using both the Dice similarity coefficient (DSC) and the Hausdorff distance (HD), and by referencing the expert contours as the ground truth. An additional 10 clinical test sets with relatively large displacement or deformation of organs were selected for verification in cancer patients. To evaluate the applicability in cancer patients, and the impact of expert intervention, three methods–HA, DLBAS, and the readjustment of the predicted data obtained via the DLBAS of the clinical test sets (HA_DLBAS)–were compared.Results: The DLBAS model (in the 20 test sets) showed reliable DSC and HD values; it also had a short contouring time of ~3 s. The average (mean ± standard deviation) DSC (0.83 ± 0.04) and HD (2.71 ± 1.01 mm) values were similar to those of previous human studies. The DLBAS was highly accurate and had no large displacement of head and neck organs. However, the DLBAS in the 10 clinical test sets showed lower DSC (0.78 ± 0.11) and higher HD (4.30 ± 3.69 mm) values than those of the test sets. The HA_DLBAS was comparable to both the HA (DSC: 0.85 ± 0.06 and HD: 2.74 ± 1.18 mm) and DLBAS presented better comparison metrics and decreased statistical deviations (DSC: 0.94 ± 0.03 and HD: 2.30 ± 0.41 mm). In addition, the contouring time of HA_DLBAS (30 min) was less than that of HA (80 min).Conclusion: In conclusion, HA_DLBAS method and the proposed DLBAS was highly consistent and robust in its performance. Thus, DLBAS has great potential as a single or supportive tool to the key process in RT planning.

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Section: Discussionsupporting

confidence: 90%

“…Therefore, there are also some previous RT studies in veterinary medicine. However, unlike these previous studies, this study focuses on segmentation, the prerequisite process of RT ( 22 , 28 – 31 ). This is because studies of automatic segmentation in dogs, particularly DLBAS, are insufficient ( 10 – 13 , 15 , 16 ).…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Based Automatic Segmentation of Head and Neck Organs for Radiation Therapy in Dogs

Park

Choi

et al. 2021

Front. Vet. Sci.

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“…This study has successfully contributed to the ongoing efforts to bring DL autosegmentation into routine clinical practice, replacing the current labor‐intensive and time‐consuming manual contouring in RT planning process. There is still a large room for improvement, 65 motivating considerable ongoing efforts, including incorporating high soft tissue contrast imaging (e.g., MRI), 51–53 developing robust algorithms (e.g., continual learning 66 ), and using large and high‐quality datasets.…”

Section: Discussionmentioning

confidence: 99%

General and custom deep learning autosegmentation models for organs in head and neck, abdomen, and male pelvis

Amjad

Thill

et al. 2022

Medical Physics

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Purpose To reduce workload and inconsistencies in organ segmentation for radiation treatment planning, we developed and evaluated general and custom autosegmentation models on computed tomography (CT) for three major tumor sites using a well‐established deep convolutional neural network (DCNN). Methods Five CT‐based autosegmentation models for 42 organs at risk (OARs) in head and neck (HN), abdomen (ABD), and male pelvis (MP) were developed using a full three‐dimensional (3D) DCNN architecture. Two types of deep learning (DL) models were separately trained using either general diversified multi‐institutional datasets or custom well‐controlled single‐institution datasets. To improve segmentation accuracy, an adaptive spatial resolution approach for small and/or narrow OARs and a pseudo scan extension approach, when CT scan length is too short to cover entire organs, were implemented. The performance of the obtained models was evaluated based on accuracy and clinical applicability of the autosegmented contours using qualitative visual inspection and quantitative calculation of dice similarity coefficient (DSC), mean distance to agreement (MDA), and time efficiency. Results The five DL autosegmentation models developed for the three anatomical sites were found to have high accuracy (DSC ranging from 0.8 to 0.98) for 74% OARs and marginally acceptable for 26% OARs. The custom models performed slightly better than the general models, even with smaller custom datasets used for the custom model training. The organ‐based approaches improved autosegmentation accuracy for small or complex organs (e.g., eye lens, optic nerves, inner ears, and bowels). Compared with traditional manual contouring times, the autosegmentation times, including subsequent manual editing, if necessary, were substantially reduced by 88% for MP, 80% for HN, and 65% for ABD models. Conclusions The obtained autosegmentation models, incorporating organ‐based approaches, were found to be effective and accurate for most OARs in the male pelvis, head and neck, and abdomen. We have demonstrated that our multianatomical DL autosegmentation models are clinically useful for radiation treatment planning.

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“…To our knowledge, there have been several related studies utilizing the patient-specific fine-tuning strategy. 12,19,20 Kim et al proposed continual DLbased segmentation for personalized adaptive radiation therapy in the HN area. However, they could not see a significant improvement in performance because there was a consistency problem between the ground truths of pCT and rpCT.…”

Section: Discussionmentioning

confidence: 99%

“…As one of the most time-intensive components of the ART workflow, 11 the recontouring of organs at risk and target structures on the rpCT is a prime target for automation through the use of DL autocontouring approaches. 12 To assist with this process at the time of adaption, we utilize the IDOL framework with the planning CTs (pCTs) and contours drawn at the beginning of treatment serving as prior information. CT datasets were from the patients with head and neck (HN) cancer treated via RT.…”

Section: Autocontouring On Rpctmentioning

confidence: 99%

Intentional deep overfit learning (IDOL): A novel deep learning strategy for adaptive radiation therapy

et al. 2021

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Purpose: Applications of deep learning (DL) are essential to realizing an effective adaptive radiotherapy (ART) workflow. Despite the promise demonstrated by DL approaches in several critical ART tasks, there remain unsolved challenges to achieve satisfactory generalizability of a trained model in a clinical setting. Foremost among these is the difficulty of collecting a task-specific training dataset with high-quality, consistent annotations for supervised learning applications. In this study, we propose a tailored DL framework for patient-specific performance that leverages the behavior of a model intentionally overfitted to a patient-specific training dataset augmented from the prior information available in an ART workflow-an approach we term Intentional Deep Overfit Learning (IDOL). Methods: Implementing the IDOL framework in any task in radiotherapy consists of two training stages: (1) training a generalized model with a diverse training dataset of N patients, just as in the conventional DL approach, and (2) intentionally overfitting this general model to a small training dataset-specific the patient of interest (N + 1) generated through perturbations and augmentations of the available task-and patient-specific prior information to establish a personalized IDOL model. The IDOL framework itself is task-agnostic and is, thus, widely applicable to many components of the ART workflow, three of which we use as a proof of concept here: the autocontouring task on replanning CTs for traditional ART, the MRI super-resolution (SR) task for MRI-guided ART, and the synthetic CT (sCT) reconstruction task for MRI-only ART. Results: In the replanning CT autocontouring task, the accuracy measured by the Dice similarity coefficient improves from 0.847 with the general model to 0.935 by adopting the IDOL model. In the case of MRI SR, the mean absolute error (MAE) is improved by 40% using the IDOL framework over the conventional model. Finally, in the sCT reconstruction task, the MAE is reduced from 68 to 22 HU by utilizing the IDOL framework. Conclusions: In this study, we propose a novel IDOL framework for ART and demonstrate its feasibility using three ART tasks.We expect the IDOL framework to be especially useful in creating personally tailored models in situations with limited availability of training data but existing prior information, which is usually true in the medical setting in general and is especially true in ART. K E Y W O R D S adaptive radiotherapy, deep learning, overfitting, personalized model 488

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Feasibility of Continual Deep Learning-Based Segmentation for Personalized Adaptive Radiation Therapy in Head and Neck Area

Cited by 23 publications

References 50 publications

Deep-Learning-Based Automatic Segmentation of Head and Neck Organs for Radiation Therapy in Dogs

Deep-Learning-Based Automatic Segmentation of Head and Neck Organs for Radiation Therapy in Dogs

General and custom deep learning autosegmentation models for organs in head and neck, abdomen, and male pelvis

Intentional deep overfit learning (IDOL): A novel deep learning strategy for adaptive radiation therapy

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