Fusing visual and clinical information for lung tissue classification in high-resolution computed tomography

Depeursinge, Adrien; Racoceanu, Daniel; Iavindrasana, Jimison; Cohen, Gilles; Platon, Alexandra; Poletti, Pierre–Alexandre; Müller, Henning

doi:10.1016/j.artmed.2010.04.006

Cited by 108 publications

(69 citation statements)

References 23 publications

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“…Depeursinge et al (2010) showed the importance of age and hematrocrit level for the classification of lung tissue types associated with interstitial lung disease. In Huo et al (2002), it was demonstrated that the patient carriers of BRCA1 and BRCA2 mutations tended to have dense breast tissue, and their mammographic patterns tended to be low in contrast, with a coarse texture.…”

Section: Opportunitiesmentioning

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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Section: Opportunitiesmentioning

confidence: 99%

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

Depeursinge

Foncubierta–Rodríguez

Ville

et al. 2014

Medical Image Analysis

Self Cite

207

146

View full text Add to dashboard Cite

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“…In a first step, the clinician can run the threedimensional categorization of the lung tissue on the undiagnosed incoming HRCT image series in order to obtain a 3D map of the lung tissue. In a second step, the clinician can retrieve similar cases from the multimedia database based both on the respective volumes of the previously segmented lung tissue sorts and on the value of the clinical parameters [5]. The workflow for the categorization of the lung tissue and case-based retrieval is detailed in Fig.…”

Section: D Lung Tissue Categorization and Case-based Retrievalmentioning

confidence: 99%

Case-based lung image categorization and retrieval for interstitial lung diseases: clinical workflows

et al. 2011

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“…Kumánovics et al demonstrated how physiological measurements such as the levels of serum are correlated with lung fibrosis [16]. Depeursinge et al showed how clinical and visual features could be fused to enhance image classification models of patients affected with interstitial lung disease [17]. Ye et al presented a framework to fuse heterogeneous data to better predict and monitor Alzheimer's disease [18] The aim of this paper is to study the associations between clinical and image features within patients with pulmonary Fig.…”

Section: Introductionmentioning

confidence: 98%

Enhancing Image Analytic Tools by Fusing Quantitative Physiological Values with Image Features

Caban

Yao²,

Mollura³

2012

J Digit Imaging

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Computer-aided diagnosis systems (CADs) can quantify the severity of diseases by analyzing a set of images and employing prior statistical models. In general, CADs have proven to be effective at providing quantitative measurements of the extent of a particular disease, thus helping physicians to better monitor the progression of cancer, infectious diseases, and other health conditions. Electronic Health Records frequently include a large amount of clinical data and medical history that can provide critical information about the underlying condition of a patient. We hypothesize that the fusion of image and clinical-physiological features can be used to enhance the accuracy of automatic image classification models. In particular, this paper shows how image analytic tools can move beyond classical image interpretation models to broader systems where image and physiological measurements are fused and used to create more generic detection models. To test our hypothesis, a CAD system capable of quantifying the severity of patients with pulmonary fibrosis has been developed. Results show that CAD systems augmented with multimodal physiological values are more robust and accurate at determining the severity of the disease.

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Fusing visual and clinical information for lung tissue classification in high-resolution computed tomography

Cited by 108 publications

References 23 publications

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

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

Case-based lung image categorization and retrieval for interstitial lung diseases: clinical workflows

Enhancing Image Analytic Tools by Fusing Quantitative Physiological Values with Image Features

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