Segmentation of heterogeneous or small FDG PET positive tissue based on a 3D-locally adaptive random walk algorithm

Onoma, Dago Pacôme; Ruan, Su; Thureau, S.; Nkhali, L.; Modzelewski, Romain; Monnehan, Georges Alain; Véra, Pierre; Gardin, I.

doi:10.1016/j.compmedimag.2014.09.007

Cited by 32 publications

(45 citation statements)

References 16 publications

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“…Figure shows that original RW(1) provides better results than the other original RW( β ) methods. The same result was reported in (Onoma et al, ). Our method retains all the properties of the original RW(1) algorithm and, in addition, uses an adaptive parameter to have more robust and user‐independent performance.…”

Section: Discussionsupporting

confidence: 90%

A fully automatic method for biological target volume segmentation of brain metastases

Stefano

Vitabile

Russo

et al. 2016

Int J Imaging Syst Tech

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Leksell Gamma Knife is a mini-invasive technique to obtain a complete destruction of cerebral lesions delivering a single high dose radiation beam. Positron Emission Tomography (PET) imaging is increasingly utilized for radiation treatment planning. Nevertheless, lesion volume delineation in PET datasets is challenging because of the low spatial resolution and high noise level of PET images. Nowadays, the biological target volume (BTV) is manually contoured on PET studies. This procedure is time expensive and operator-dependent. In this article, a fully automatic algorithm for the BTV delineation based on random walks (RW) on graphs is proposed. The results are compared with the outcomes of the original RW method, 40% thresholding method, region growing method, and fuzzy c-means clustering method. To validate the effectiveness of the proposed approach in a clinical environment, BTV segmentation on 18 patients with cerebral metastases is performed. Experimental results show that the segmentation algorithm is accurate and has real-time performance satisfying the physician requirements in a radiotherapy environment

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Section: Discussionsupporting

confidence: 90%

A fully automatic method for biological target volume segmentation of brain metastases

Stefano

Vitabile

Russo

et al. 2016

Int J Imaging Syst Tech

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“…In comparison with the work on PET segmentation of heterogeneous tumor described earlier by various groups, 12,16,33 the present work does not describe elaborate mathematical models. Instead, this work was aimed at being simple and clinically implementable using widely used 3D Slicer software.…”

Section: Discussionmentioning

confidence: 99%

“…The methods range from simple to complex methods. 1,3,4 The segmentation methods include but are not limited to constant and adaptive threshold methods, [5][6][7][8][9] region growing methods, [10][11][12] gradient-based methods, [13][14][15] fuzzy models, [16][17][18] and Gaussian mixture modeling. 10,19 All these methods offer different compromises in terms of versatility and performance and compare well against manual segmentations by clinicians or the pathological measurements with varying rates of success.…”

Section: Introductionmentioning

confidence: 99%

Hybrid positron emission tomography segmentation of heterogeneous lung tumors using 3D Slicer: improved GrowCut algorithm with threshold initialization

et al. 2017

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Abstract. This paper presents an improved GrowCut (IGC), a positron emission tomography-based segmentation algorithm, and tests its clinical applicability. Contrary to the traditional method that requires the user to provide the initial seeds, the IGC algorithm starts with a threshold-based estimate of the tumor and a threedimensional morphologically grown shell around the tumor as the foreground and background seeds, respectively. The repeatability of IGC from the same observer at multiple time points was compared with the traditional GrowCut algorithm. The algorithm was tested in 11 nonsmall cell lung cancer lesions and validated against the clinician-defined manual contour and compared against the clinically used 25% of the maximum standardized uptake value [SUV-(max)], 40% SUV max , and adaptive threshold methods. The time to edit IGC-defined functional volume to arrive at the gross tumor volume (GTV) was compared with that of manual contouring. The repeatability of the IGC algorithm was very high compared with the traditional GrowCut (p ¼ 0.003) and demonstrated higher agreement with the manual contour with respect to threshold-based methods. Compared with manual contouring, editing the IGC achieved the GTV in significantly less time (p ¼ 0.11). The IGC algorithm offers a highly repeatable functional volume and serves as an effective initial guess that can well minimize the time spent on labor-intensive manual contouring.

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“…This approach has been also successfully applied to hybrid imaging data, such as PET/CT and PET/MRI [216]. Similarly, random walk approaches have been proven to be effective delineation tools especially over noisy images [216][217][218]. A comparative study of 13 PET segmentation methods over 157 simulated, phantom and clinical PET images was presented in Hatt et al [207]; here, a method built on a convolutional neural network (CNN) was found to be the most accurate.…”

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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Segmentation of heterogeneous or small FDG PET positive tissue based on a 3D-locally adaptive random walk algorithm

Cited by 32 publications

References 16 publications

A fully automatic method for biological target volume segmentation of brain metastases

A fully automatic method for biological target volume segmentation of brain metastases

Hybrid positron emission tomography segmentation of heterogeneous lung tumors using 3D Slicer: improved GrowCut algorithm with threshold initialization

Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

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