Diffuse Parenchymal Lung Diseases: 3D Automated Detection in MDCT

Fétita, Catalin; Chang-Chien, Kuang-Che; Brillet, Pierre-Yves; Prêteux, Françoise; Greniér, Philippe

doi:10.1007/978-3-540-75757-3_100

Cited by 10 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Texture-based identification and characterization of interstitial pneumonia patterns and diffuse lung diseases in MDCT has been investigated by several research groups during the past 5 years (Xu et al, 2005;Xu et al, 2006b;Xu et al, 2006a;Fetita et al, 2007a;Fetita et al, 2007b;Korfiatis et al, 2008;Boehm et al, 2008;Chang-Chien et al, 2009;Mariolis et al, 2010a;Korfiatis et al, 2010;Mariolis et al, 2010b;van Rikxoort et al, 2011). Interstitial and diffuse lung diseases create large regions of altered tissue (e.g., fibrosis, ground glass, emphysema, micronodules, consolidation) with well-defined texture properties, which are best described in terms of texture properties (Webb et al, 2001).…”

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

207

146

View full text Add to dashboard Cite

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.

show abstract

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

207

146

View full text Add to dashboard Cite

show abstract

“…Also, texture analysis in three dimensions can be considered. To this end, high resolution volumetric data is needed as used in Fetita, Chang-Chien, Brillet, Prêteux, and Grenier (2007). Using information from the slices above and below the slice under consideration can prove to be helpful.…”

Section: Discussionmentioning

confidence: 99%

Image based diagnostic aid system for interstitial lung diseases

Tolouee¹,

Moghaddam²,

Forouzanfar³

et al. 2011

Expert Systems with Applications

View full text Add to dashboard Cite

“…Conventional methods for automated lung nodule segmentation in CT images 4 , 5 commonly consist of two steps: the detection of nodule locations and then the segmentation of the detected nodules from the surrounding lung parenchyma. 6 The features characterizing nodule intensity, textures, and morphologies are usually extracted to differentiate nodules from other lung structures during nodule detection.…”

Section: Introductionmentioning

confidence: 99%

Hybrid U‐Net‐based deep learning model for volume segmentation of lung nodules in CT images

et al. 2022

View full text Add to dashboard Cite

Objective Accurate segmentation of the lung nodule in computed tomography images is a critical component of a computer‐assisted lung cancer detection/diagnosis system. However, lung nodule segmentation is a challenging task due to the heterogeneity of nodules. This study is to develop a hybrid deep learning (H‐DL) model for the segmentation of lung nodules with a wide variety of sizes, shapes, margins, and opacities. Materials and methods A dataset collected from Lung Image Database Consortium image collection containing 847 cases with lung nodules manually annotated by at least two radiologists with nodule diameters greater than 7 mm and less than 45 mm was randomly split into 683 training/validation and 164 independent test cases. The 50% consensus consolidation of radiologists' annotation was used as the reference standard for each nodule. We designed a new H‐DL model combining two deep convolutional neural networks (DCNNs) with different structures as encoders to increase the learning capabilities for the segmentation of complex lung nodules. Leveraging the basic symmetric U‐shaped architecture of U‐Net, we redesigned two new U‐shaped deep learning (U‐DL) models that were expanded to six levels of convolutional layers. One U‐DL model used a shallow DCNN structure containing 16 convolutional layers adapted from the VGG‐19 as the encoder, and the other used a deep DCNN structure containing 200 layers adapted from DenseNet‐201 as the encoder, while the same decoder with only one convolutional layer at each level was used in both U‐DL models, and we referred to them as the shallow and deep U‐DL models. Finally, an ensemble layer was used to combine the two U‐DL models into the H‐DL model. We compared the effectiveness of the H‐DL, the shallow U‐DL and the deep U‐DL models by deploying them separately to the test set. The accuracy of volume segmentation for each nodule was evaluated by the 3D Dice coefficient and Jaccard index (JI) relative to the reference standard. For comparison, we calculated the median and minimum of the 3D Dice and JI over the individual radiologists who segmented each nodule, referred to as M‐Dice, min‐Dice, M‐JI, and min‐JI. Results For the 164 test cases with 327 nodules, our H‐DL model achieved an average 3D Dice coefficient of 0.750 ± 0.135 and an average JI of 0.617 ± 0.159. The radiologists' average M‐Dice was 0.778 ± 0.102, and the average M‐JI was 0.651 ± 0.127; both were significantly higher than those achieved by the H‐DL model (p < 0.05). The radiologists' average min‐Dice (0.685 ± 0.139) and the average min‐JI (0.537 ± 0.153) were significantly lower than those achieved by the H‐DL model (p < 0.05). The results indicated that the H‐DL model approached the average performance of radiologists and was superior to the radiologist whose manual segmentation had the min‐Dice and min‐JI. Moreover, the average Dice and average JI achieved by the H‐DL model were significantly higher than those achieved by the individual shallow U‐DL model (Dice of 0.745 ± 0.139, JI of 0.611 ± 0.1...

show abstract

Diffuse Parenchymal Lung Diseases: 3D Automated Detection in MDCT

Cited by 10 publications

References 10 publications

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

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

Image based diagnostic aid system for interstitial lung diseases

Hybrid U‐Net‐based deep learning model for volume segmentation of lung nodules in CT images

Contact Info

Product

Resources

About