Clinical implementation of deep-learning based auto-contouring tools–Experience of three French radiotherapy centers

Robert, Charlotte; Muñoz, A.; Moreau, Delphine; Mazurier, J.; Sidorski, G.; Gasnier, A.; Beldjoudi, G.; Grégoire, Vincent; Deutsch, Éric; Meyer, P.; Simon, L.

doi:10.1016/j.canrad.2021.06.023

Cited by 24 publications

(17 citation statements)

References 55 publications

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“…Deep learning-based (DL) automatic contouring for radiotherapy has evolved over the past years leading to clinical implementation at many institutes and for many treatment sites. Significant time saving compared to both manual contouring and atlas-based contouring methods is achieved [1] , [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] . Even though the majority of current clinical tools still use atlas-based auto-contouring, DL-contouring is being offered more often recently [5] , [9] .…”

Section: Introductionmentioning

confidence: 99%

“…Significant time saving compared to both manual contouring and atlas-based contouring methods is achieved [1] , [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] . Even though the majority of current clinical tools still use atlas-based auto-contouring, DL-contouring is being offered more often recently [5] , [9] . However, clinical implementation of such an automatic contouring software requires thorough validation and quality assurance (QA) [9] , [11] , [12] , [13] , [14] .…”

Section: Introductionmentioning

confidence: 99%

“…Even though the majority of current clinical tools still use atlas-based auto-contouring, DL-contouring is being offered more often recently [5] , [9] . However, clinical implementation of such an automatic contouring software requires thorough validation and quality assurance (QA) [9] , [11] , [12] , [13] , [14] . User-adjustments evaluated in routine clinical practice give insight into the real-world model performance.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Real-world analysis of manual editing of deep learning contouring in the thorax region

Vaassen

Boukerroui

Looney

et al. 2022

Physics and Imaging in Radiation Oncology

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Real-world analysis of manual editing of deep learning contouring in the thorax region

Vaassen

Boukerroui

Looney

et al. 2022

Physics and Imaging in Radiation Oncology

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“…Contouring is often performed manually by trained radiation oncologists [ 4 ]. This task, also referred to as segmentation or delineation, is highly time-consuming and presents a subjective component [ 5 , 6 , 7 , 8 ]. An expert can spend over four hours on a single HN case [ 3 , 9 ], which may delay treatment increasing risks of local recurrence and mortality.…”

Section: Introductionmentioning

confidence: 99%

“…An expert can spend over four hours on a single HN case [ 3 , 9 ], which may delay treatment increasing risks of local recurrence and mortality. Furthermore, this process is thoroughly subjected to inter- and intra-practitioner variabilities [ 6 , 9 , 10 ] driven by diverse factors such as experience, availability, quality, and interpretation of diagnostic imaging [ 4 ]. The limited soft tissue contrast of computed tomography (CT) images is also a substantial problem in HN delineation [ 10 , 11 ], as many OARs have similar densities to fat, muscle, or other surrounding tissues.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Segmentation of Head and Neck Organs-at-Risk with Clinical Partially Labeled Data

Cubero

Castelli

Simon

et al. 2022

Entropy

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Radiotherapy is one of the main treatments for localized head and neck (HN) cancer. To design a personalized treatment with reduced radio-induced toxicity, accurate delineation of organs at risk (OAR) is a crucial step. Manual delineation is time- and labor-consuming, as well as observer-dependent. Deep learning (DL) based segmentation has proven to overcome some of these limitations, but requires large databases of homogeneously contoured image sets for robust training. However, these are not easily obtained from the standard clinical protocols as the OARs delineated may vary depending on the patient’s tumor site and specific treatment plan. This results in incomplete or partially labeled data. This paper presents a solution to train a robust DL-based automated segmentation tool exploiting a clinical partially labeled dataset. We propose a two-step workflow for OAR segmentation: first, we developed longitudinal OAR-specific 3D segmentation models for pseudo-contour generation, completing the missing contours for some patients; with all OAR available, we trained a multi-class 3D convolutional neural network (nnU-Net) for final OAR segmentation. Results obtained in 44 independent datasets showed superior performance of the proposed methodology for the segmentation of fifteen OARs, with an average Dice score coefficient and surface Dice similarity coefficient of 80.59% and 88.74%. We demonstrated that the model can be straightforwardly integrated into the clinical workflow for standard and adaptive radiotherapy.

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Deep learning‐based classification of organs at risk and delineation guideline in pelvic cancer radiation therapy

Lempart

Scherman

Nilsson

et al. 2023

J Applied Clin Med Phys

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Deep learning (DL) models for radiation therapy (RT) image segmentation require accurately annotated training data. Multiple organ delineation guidelines exist; however, information on the used guideline is not provided with the delineation. Extraction of training data with coherent guidelines can therefore be challenging. We present a supervised classification method for pelvis structure delineations where bowel cavity, femoral heads, bladder, and rectum data, with two guidelines, were classified. The impact on DL‐based segmentation quality using mixed guideline training data was also demonstrated. Bowel cavity was manually delineated on CT images for anal cancer patients (n = 170) according to guidelines Devisetty and RTOG. The DL segmentation quality from using training data with coherent or mixed guidelines was investigated. A supervised 3D squeeze‐and‐excite SENet‐154 model was trained to classify two bowel cavity delineation guidelines. In addition, a pelvis CT dataset with manual delineations from prostate cancer patients (n = 1854) was used where data with an alternative guideline for femoral heads, rectum, and bladder were generated using commercial software. The model was evaluated on internal (n = 200) and external test data (n = 99). By using mixed, compared to coherent, delineation guideline training data mean DICE score decreased 3% units, mean Hausdorff distance (95%) increased 5 mm and mean surface distance (MSD) increased 1 mm. The classification of bowel cavity test data achieved 99.8% unweighted classification accuracy, 99.9% macro average precision, 97.2% macro average recall, and 98.5% macro average F1. Corresponding metrics for the pelvis internal test data were all 99% or above and for the external pelvis test data they were 96.3%, 96.6%, 93.3%, and 94.6%. Impaired segmentation performance was observed for training data with mixed guidelines. The DL delineation classification models achieved excellent results on internal and external test data. This can facilitate automated guideline‐specific data extraction while avoiding the need for consistent and correct structure labels.

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Clinical implementation of deep-learning based auto-contouring tools–Experience of three French radiotherapy centers

Cited by 24 publications

References 55 publications

Real-world analysis of manual editing of deep learning contouring in the thorax region

Real-world analysis of manual editing of deep learning contouring in the thorax region

Deep Learning-Based Segmentation of Head and Neck Organs-at-Risk with Clinical Partially Labeled Data

Deep learning‐based classification of organs at risk and delineation guideline in pelvic cancer radiation therapy

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