3D lung image retrieval using localized features

Depeursinge, Adrien; Zrimec, Tatjana; Busayarat, Sata; Müller, Henning

doi:10.1117/12.877943

Cited by 19 publications

(20 citation statements)

References 28 publications

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“…The locations were chosen according to the predominant pattern sites of classic UIP (see Table 1). First, semi-automated segmentation of the lung volumes was carried out with a graphical user interface [24]. The user initiated a region growing algorithm from a seed point placed inside the lungs.…”

Section: Methodsmentioning

confidence: 99%

Automated Classification of Usual Interstitial Pneumonia Using Regional Volumetric Texture Analysis in High-Resolution Computed Tomography

Depeursinge¹,

Chin

Leung

et al. 2015

Investigative Radiology

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Objectives We propose a novel computational approach for the automated classification of classic versus atypical usual interstitial pneumonia (UIP). Materials and Methods 33 patients with UIP were enrolled in this study. They were classified as classic versus atypical UIP by a consensus of two thoracic radiologists with more than 15 years of experience using the American Thoracic Society evidence–based guidelines for CT diagnosis of UIP. Two cardiothoracic fellows with one year of subspecialty training provided independent readings. The system is based on regional characterization of the morphological tissue properties of lung using volumetric texture analysis of multiple detector CT images. A simple digital atlas with 36 lung subregions is used to locate texture properties, from which the responses of multi-directional Riesz wavelets are obtained. Machine learning is used to aggregate and to map the regional texture attributes to a simple score that can be used to stratify patients with UIP into classic and atypical subtypes. Results We compared the predictions based on regional volumetric texture analysis with the ground truth established by expert consensus. The area under the receiver operating characteristic curve of the proposed score was estimated to be 0.81 using a leave-one-patient-out cross-validation, with high specificity for classic UIP. The performance of our automated method was found to be similar to that of the two fellows and to the agreement between experienced chest radiologists reported in the literature. However, the errors of our method and the fellows occurred on different cases, which suggests that combining human and computerized evaluations may be synergistic. Conclusions Our results are encouraging and suggest that an automated system may be useful in routine clinical practice as a diagnostic aid for identifying patients with complex lung disease such as classic UIP, obviating the need for invasive surgical lung biopsy and its associated risks.

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Section: Methodsmentioning

confidence: 99%

Automated Classification of Usual Interstitial Pneumonia Using Regional Volumetric Texture Analysis in High-Resolution Computed Tomography

Depeursinge¹,

Chin

Leung

et al. 2015

Investigative Radiology

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“…Comparisons between 2-D and 3-D approaches showed improved classification performance in Xu et al (2005) and Xu et al (2006b). The texture properties vary strongly with the anatomical location which was taken into account in only few articles (Depeursinge et al, 2011b andDepeursinge et al, 2013). The sections of the bronchus and vessel decrease with the distance to the mediastinum.…”

Section: Lungmentioning

confidence: 99%

Three-dimensional solid texture analysis in biomedical imaging: Review and opportunities

Depeursinge

Foncubierta–Rodríguez

Ville

et al. 2014

Medical Image Analysis

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a b s t r a c tThree-dimensional computerized characterization of biomedical solid textures is key to large-scale and high-throughput screening of imaging data. Such data increasingly become available in the clinical and research environments with an ever increasing spatial resolution.In this text we exhaustively analyze the state-of-the-art in 3-D biomedical texture analysis to identify the specific needs of the application domains and extract promising trends in image processing algorithms. The geometrical properties of biomedical textures are studied both in their natural space and on digitized lattices. It is found that most of the tissue types have strong multi-scale directional properties, that are well captured by imaging protocols with high resolutions and spherical spatial transfer functions. The information modeled by the various image processing techniques is analyzed and visualized by displaying their 3-D texture primitives. We demonstrate that non-convolutional approaches are expected to provide best results when the size of structures are inferior to five voxels. For larger structures, it is shown that only multi-scale directional convolutional approaches that are non-separable allow for an unbiased modeling of 3-D biomedical textures. With the increase of high-resolution isotropic imaging protocols in clinical routine and research, these models are expected to best leverage the wealth of 3-D biomedical texture analysis in the future. Future research directions and opportunities are proposed to efficiently model personalized image-based phenotypes of normal biomedical tissue and its alterations. The integration of the clinical and genomic context is expected to better explain the intra class variation of healthy biomedical textures. Using texture synthesis, this provides the exciting opportunity to simulate and visualize texture atlases of normal ageing process and disease progression for enhanced treatment planning and clinical care management.

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“…Therefore, an atlas-based segmentation in regions with similar vessels size was selected. This atlas, proposed by Depeursinge et al, 16 allows to compare vessel properties in similar localization between patients. Moreover, this atlas can be computed fully automatically because it relies on the geometry of the lung, and no human interaction is needed.…”

Section: Atlas Of the Lungmentioning

confidence: 99%

“…Atlas-based segmentation presented by Depeursinge et al16 The atlas is built by dividing the lungs geometrically along 4 axes. The intersection of these divisions form the 36 regions of the atlas.…”

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confidence: 99%

Pulmonary embolism detection using localized vessel-based features in dual energy CT

Cid

Depeursinge

Foncubierta–Rodríguez

et al. 2015

SPIE Proceedings

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Pulmonary embolism (PE) affects up to 600,000 patients and contributes to at least 100,000 deaths every year in the United States alone. Diagnosis of PE can be difficult as most symptoms are unspecific and early diagnosis is essential for successful treatment. Computed Tomography (CT) images can show morphological anomalies that suggest the existance of PE. Various image-based procedures have been proposed for improving computer-aided diagnosis of PE. We propose a novel method for detecting PE based on localized vessel-based features computed in Dual Energy CT (DECT) images. DECT provides 4D data indexed by the three spatial coordinates and the energy level. The proposed features encode the variation of the Hounsfield Units across the different levels and the CT attenuation related to the amount of iodine contrast in each vessel. A local classification of the vessels is obtained through the classification of these features. Moreover, the localization of the vessel in the lung provides better comparison between patients. Results show that the simple features designed are able to classify pulmonary embolism patients with an AUC (area under the receiver operating curve) of 0.71 on a lobe basis. Prior segmentation of the lung lobes is not necessary because an automatic atlas-based segmentation obtains similar AUC levels (0.65) for the same dataset. The automatic atlas reaches 0.80 AUC in a larger dataset with more control cases.

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3D lung image retrieval using localized features

Cited by 19 publications

References 28 publications

Automated Classification of Usual Interstitial Pneumonia Using Regional Volumetric Texture Analysis in High-Resolution Computed Tomography

Automated Classification of Usual Interstitial Pneumonia Using Regional Volumetric Texture Analysis in High-Resolution Computed Tomography

Three-dimensional solid texture analysis in biomedical imaging: Review and opportunities

Pulmonary embolism detection using localized vessel-based features in dual energy CT

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