Combining deep learning with anatomical analysis for segmentation of the portal vein for liver SBRT planning

Ibragimov, Bulat; Toesca, Diego A.S.; Chang, Daniel T.; Koong, Albert C.; Liu, Xing

doi:10.1088/1361-6560/aa9262

Cited by 79 publications

(57 citation statements)

References 51 publications

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“…Improved computing power and training of neural networks have made deep learning methods more readily available for contouring purposes [20]. Several studies have already shown the potential of CNNs for HN contouring [21,22] and for other sites [23][24][25].…”

mentioning

confidence: 99%

Improving automatic delineation for head and neck organs at risk by Deep Learning Contouring

Dijk

Bosch

Aljabar

et al. 2020

Radiotherapy and Oncology

173

163

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Introduction: Adequate head and neck (HN) organ-at-risk (OAR) delineation is crucial for HN radiotherapy and for investigating the relationships between radiation dose to OARs and radiation-induced side effects. The automatic contouring algorithms that are currently in clinical use, such as atlas-based contouring (ABAS), leave room for improvement. The aim of this study was to use a comprehensive evaluation methodology to investigate the performance of HN OAR auto-contouring when using deep learning contouring (DLC), compared to ABAS. Methods: The DLC neural network was trained on 589 HN cancer patients. DLC was compared to ABAS by providing each method with an independent validation cohort of 104 patients, which had also been manually contoured. For each of the 22 OAR contours-glandular, upper digestive tract and central nervous system (CNS)-related structures-the dice similarity coefficient (DICE), and absolute mean and max dose differences (|Dmean-dose| and |Dmax-dose|) performance measures were obtained. For a subset of 7 OARs, an evaluation of contouring time, inter-observer variation and subjective judgement was performed. Results: DLC resulted in equal or significantly improved quantitative performance measures in 19 out of 22 OARs, compared to the ABAS (DICE/|Dmean dose|/|Dmax dose|: 0.59/4.2/4.1 Gy (ABAS); 0.74/1.1/0.8 Gy (DLC)). The improvements were mainly for the glandular and upper digestive tract OARs. DLC significantly reduced the delineation time for the inexperienced observer. The subjective evaluation showed that DLC contours were more often preferable to the ABAS contours overall, were considered to be more precise, and more often confused with manual contours. Manual contours still outperformed both DLC and ABAS; however, DLC results were within or bordering the inter-observer variability for the manual edited contours in this cohort. Conclusion: The DLC, trained on a large HN cancer patient cohort, outperformed the ABAS for the majority of HN OARs.

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confidence: 99%

Improving automatic delineation for head and neck organs at risk by Deep Learning Contouring

Dijk

Bosch

Aljabar

et al. 2020

Radiotherapy and Oncology

173

163

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“…DL, which to date has achieved broad success in image analysis and computer vision, appears to resolve the time and computation constraints inherent in traditional atlasbased methods. DL algorithms have been trained to segment cancer and organ-at-risk (OAR) structures in the head and neck (26)(27)(28)(29)(30)(31), brain (32)(33)(34), abdomen (35,36), thorax (37)(38)(39)(40)(41)(42), spinal cord (43), breast (44)(45)(46), and pelvis (47)(48)(49)(50) at accuracies indistinguishable from human experts and with clinical workflow implementation and validation in some cases. Several studies have concluded that DL is more accurate than other algorithms with respect to the Dice Similarity Coefficient (DSC) (28,37,47,51,52), a standard volume-overlap evaluation metric used in segmentation literature (53).…”

Section: Auto Segmentationmentioning

confidence: 99%

Artificial intelligence in radiation oncology treatment planning: a brief overview

Kiser¹,

Fuller²,

Reed³

2019

J Med Artif Intell

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Among medical specialties, radiation oncology has long been an innovator and early adopter of therapeutic technologies. This specialty is now situated in prime position to be revolutionized by advances in artificial intelligence (AI), especially machine and deep learning. AI has been investigated by radiation oncologists and physicists in both general and niche radiotherapy planning tasks and has often demonstrated performance that is indistinguishable from human experts, while substantially shortening the time required to complete these tasks. We sought to review applications of AI to domains germane to radiation oncology, namely: image segmentation, treatment plan generation and optimization, normal tissue complication probability modeling, quality assurance (QA), and adaptive re-planning. We sought likewise to consider obstacles to AI adoption in the radiotherapy clinic, now primarily political, legal, and ethical rather than technical in nature.

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“…Multiple level visual features are extracted and predictions are made automatically. There has been increasing interest in applying CNN to radiation therapy . The group (Ibragimov and Xing) pioneered the introduction of CNN into radiotherapy contouring and achieved better or similar results in H&N site compared with state‐of‐the‐art algorithms.…”

Section: Introductionmentioning

confidence: 99%

Technical Note: More accurate and efficient segmentation of organs‐at‐risk in radiotherapy with convolutional neural networks cascades

et al. 2018

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Purpose: Manual delineation of organs-at-risk (OARs) in radiotherapy is both time-consuming and subjective. Automated and more accurate segmentation is of the utmost importance in clinical application. The purpose of this study is to further improve the segmentation accuracy and efficiency with a novel network named Convolutional Neural Networks (CNN) Cascades. Methods: CNN Cascades was a two-step, coarse-to-fine approach that consisted of a Simple Region Detector (SRD) and a Fine Segmentation Unit (FSU). The SRD first used a relative shallow network to define the region of interest (ROI) where the organ was located, and then the FSU took the smaller ROI as input and adopted a deep network for fine segmentation. The imaging data (14,651 slices) of 100 head-and-neck patients with segmentations were used for this study. The performance was compared with the state-of-the-art single CNN in terms of accuracy with metrics of Dice similarity coefficient (DSC) and Hausdorff distance (HD) values. Results: The proposed CNN Cascades outperformed the single CNN on accuracy for each OAR. Similarly, for the average of all OARs, it was also the best with mean DSC of 0.90 (SRD: 0.86, FSU: 0.87, and U-Net: 0.85) and the mean HD of 3.0 mm (SRD: 4.0, FSU: 3.6, and U-Net: 4.4). Meanwhile, the CNN Cascades reduced the mean segmentation time per patient by 48% (FSU) and 5% (U-Net), respectively. Conclusions: The proposed two-step network demonstrated superior performance by reducing the input region. This potentially can be an effective segmentation method that provides accurate and consistent delineation with reduced clinician interventions for clinical applications as well as for quality assurance of a multi-center clinical trial.

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Combining deep learning with anatomical analysis for segmentation of the portal vein for liver SBRT planning

Cited by 79 publications

References 51 publications

Improving automatic delineation for head and neck organs at risk by Deep Learning Contouring

Improving automatic delineation for head and neck organs at risk by Deep Learning Contouring

Artificial intelligence in radiation oncology treatment planning: a brief overview

Technical Note: More accurate and efficient segmentation of organs‐at‐risk in radiotherapy with convolutional neural networks cascades

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