Interactive contour delineation of organs at risk in radiotherapy: Clinical evaluation on NSCLC patients

Dolz, José; Kirişli, Hortense A.; Fechter, Tobias; Karnitzki, S.; Oehlke, Oliver; Nestle, Ursula; Vermandel, Maximilien; Massoptier, Laurent

doi:10.1118/1.4947484

Cited by 25 publications

(19 citation statements)

References 30 publications

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“…Despite the known drawbacks of manual and semi-automated approaches and the emerging success of automated contouring, to date, the latter solution is still underrepresented in clinical routine [151,[219][220][221]. This indicates the necessity to extend the evaluation and cross-validation of popular delineation approaches in a large-scale multi-center environment.…”

Section: Delineationmentioning

confidence: 99%

Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

et al. 2018

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Medical imaging has evolved from a pure visualization tool to representing a primary source of analytic approaches toward in vivo disease characterization. Hybrid imaging is an integral part of this approach, as it provides complementary visual and quantitative information in the form of morphological and functional insights into the living body. As such, non-invasive imaging modalities no longer provide images only, but data, as stated recently by pioneers in the field. Today, such information, together with other, non-imaging medical data creates highly heterogeneous data sets that underpin the concept of medical big data. While the exponential growth of medical big data challenges their processing, they inherently contain information that benefits a patient-centric personalized healthcare. Novel machine learning approaches combined with high-performance distributed cloud computing technologies help explore medical big data. Such exploration and subsequent generation of knowledge require a profound understanding of the technical challenges. These challenges increase in complexity when employing hybrid, aka dual-or even multi-modality image data as input to big data repositories. This paper provides a general insight into medical big data analysis in light of the use of hybrid imaging information. First, hybrid imaging is introduced (see further contributions to this special Research Topic), also in the context of medical big data, then the technological background of machine learning as well as state-of-the-art distributed cloud computing technologies are presented, followed by the discussion of data preservation and data sharing trends. Joint data exploration endeavors in the context of in vivo radiomics and hybrid imaging will be presented. Standardization challenges of imaging protocol, delineation, feature engineering, and machine learning evaluation will be detailed. Last, the paper will provide an outlook into the future role of hybrid imaging in view of personalized medicine, whereby a focus will be given to the derivation of prediction models as part of clinical decision support systems, to which machine learning approaches and hybrid imaging can be anchored.

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Section: Delineationmentioning

confidence: 99%

Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

et al. 2018

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“…The clinical applicability of automatic contouring software is well-reported for regions such as head and neck, breast, and abdomen [2][3][4][5][6]. For the lung, there are a number of studies reporting automatic contouring [7][8][9][10][11][12][13][14]. Some focus solely on a single method and OAR or the Gross Tumor Volume (GTV) and only evaluate the accuracy of the method without any clinical implications such as time gain.…”

Section: Introductionmentioning

confidence: 99%

“…Two studies address the usability of atlas-based contouring for the thorax OARs [9,14]. Dolz et al provides a framework for a region-based contouring technique in clinical practice [13], they advise further investigation into more accurate atlas selection methods to improve the clinical usability. All these methods should be followed by manual correction of the imperfections of the software contouring with the techniques currently available to assess the clinical usability of these methods.…”

Section: Introductionmentioning

confidence: 99%

Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer

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et al. 2018

Radiotherapy and Oncology

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“…Although considerable attention has been devoted to the automated segmentation of anatomical structures in CT, literature on esophagus segmentation remains scarce, with results far from being fully satisfactory. Existing approaches that can successfully segment different structures in CT often fail to segment the esophagus . One of the main reasons for this is the absence of consistent intensity contrast between the esophagus and surrounding tissues in thoracic CT scans.…”

Section: Introductionmentioning

confidence: 99%

Esophagus segmentation in CT via 3D fully convolutional neural network and random walk

et al. 2017

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Purpose: Precise delineation of organs at risk is a crucial task in radiotherapy treatment planning for delivering high doses to the tumor while sparing healthy tissues. In recent years, automated segmentation methods have shown an increasingly high performance for the delineation of various anatomical structures. However, this task remains challenging for organs like the esophagus, which have a versatile shape and poor contrast to neighboring tissues. For human experts, segmenting the esophagus from CT images is a time-consuming and error-prone process. To tackle these issues, we propose a random walker approach driven by a 3D fully convolutional neural network (CNN) to automatically segment the esophagus from CT images. Methods: First, a soft probability map is generated by the CNN. Then, an active contour model (ACM) is fitted to the CNN soft probability map to get a first estimation of the esophagus location. The outputs of the CNN and ACM are then used in conjunction with a probability model based on CT Hounsfield (HU) values to drive the random walker. Training and evaluation were done on 50 CTs from two different datasets, with clinically used peer-reviewed esophagus contours. Results were assessed regarding spatial overlap and shape similarity. Results: The esophagus contours generated by the proposed algorithm showed a mean Dice coefficient of 0.76 AE 0.11, an average symmetric square distance of 1.36 AE 0.90 mm, and an average Hausdorff distance of 11.68 AE 6.80, compared to the reference contours. These results translate to a very good agreement with reference contours and an increase in accuracy compared to existing methods. Furthermore, when considering the results reported in the literature for the publicly available Synapse dataset, our method outperformed all existing approaches, which suggests that the proposed method represents the current state-of-the-art for automatic esophagus segmentation. Conclusion: We show that a CNN can yield accurate estimations of esophagus location, and that the results of this model can be refined by a random walk step taking pixel intensities and neighborhood relationships into account. One of the main advantages of our network over previous methods is that it performs 3D convolutions, thus fully exploiting the 3D spatial context and performing an efficient volume-wise prediction. The whole segmentation process is fully automatic and yields esophagus delineations in very good agreement with the gold standard, showing that it can compete with previously published methods.

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Interactive contour delineation of organs at risk in radiotherapy: Clinical evaluation on NSCLC patients

Cited by 25 publications

References 30 publications

Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

Clinical evaluation of atlas and deep learning based automatic contouring for lung cancer

Esophagus segmentation in CT via 3D fully convolutional neural network and random walk

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