Volumetric characterization of human patellar cartilage matrix on phase contrast x-ray computed tomography

Abidin, Anas Z.; Nagarajan, Mahesh; Checefsky, Walter A.; Coan, Paola; Diémoz, Paul C.; Hobbs, Susan K.; Huber, Markus; Wismüller, Axel

doi:10.1117/12.2082084

Cited by 2 publications

(3 citation statements)

References 32 publications

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“…Few studies have investigated the utility of machine learning in Phase contrast imaging data [47, 20, 21]. In previous works, we demonstrated the application of textural characterization of Phase contrast imaging data and its application in a computer aided diagnostic framework.…”

Section: Discussionmentioning

confidence: 99%

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Deep transfer learning for characterizing chondrocyte patterns in phase contrast X-Ray computed tomography images of the human patellar cartilage

Abidin

Deng

DSouza

et al. 2018

Computers in Biology and Medicine

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Phase contrast X-ray computed tomography (PCI-CT) has been demonstrated to be effective for visualization of the human cartilage matrix at micrometer resolution, thereby capturing osteoarthritis induced changes to chondrocyte organization. This study aims to systematically assess the efficacy of deep transfer learning methods for classifying between healthy and diseased tissue patterns. We extracted features from two different convolutional neural network architectures, CaffeNet and Inception-v3 for characterizing such patterns. These features were quantitatively evaluated in a classification task measured by the area (AUC) under the Receiver Operating Characteristic (ROC) curve as well as qualitative visualization through a dimension reduction approach t-Distributed Stochastic Neighbor Embedding (t-SNE). The best classification performance, for CaffeNet, was observed when using features from the last convolutional layer and the last fully connected layer (AUCs >0.91). Meanwhile, off-the-shelf features from Inception-v3 produced similar classification performance (AUC >0.95). Visualization of features from these layers further confirmed adequate characterization of chondrocyte patterns for reliably distinguishing between healthy and osteoarthritic tissue classes. Such techniques, can be potentially used for detecting the presence of osteoarthritis related changes in the human patellar cartilage.

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Section: Discussionmentioning

confidence: 99%

“…We have previously [20, 21] shown that PCI-CT images can be characterized effectively with 2D or 3D texture features, in a computer aided diagnostics framework. In this study, we explore the use of deep learning for characterizing chondrocyte organization of the cartilage matrix visualized in these images.…”

Section: Introductionmentioning

confidence: 99%

Deep transfer learning for characterizing chondrocyte patterns in phase contrast X-Ray computed tomography images of the human patellar cartilage

Abidin

Deng

DSouza

et al. 2018

Computers in Biology and Medicine

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“…In previous studies [6] we have used a range of thresholds, which capture information in high dimensional features. Having high dimensional feature representations is an advantage as can be seen here in Figure 1.…”

Section: Methodsmentioning

confidence: 99%

Classification of micro-CT images using 3D characterization of bone canal patterns in human osteogenesis imperfecta

et al. 2017

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Few studies have analyzed the microstructural properties of bone in cases of Osteogenenis Imperfecta (OI), or ‘brittle bone disease’. Current approaches mainly focus on bone mineral density measurements as an indirect indicator of bone strength and quality. It has been shown that bone strength would depend not only on composition but also structural organization. This study aims to characterize 3D structure of the cortical bone in high-resolution micro CT images. A total of 40 bone fragments from 28 subjects (13 with OI and 15 healthy controls) were imaged using micro tomography using a synchrotron light source (SRμCT). Minkowski functionals - volume, surface, curvature, and Euler characteristics - describing the topological organization of the bone were computed from the images. The features were used in a machine learning task to classify between healthy and OI bone. The best classification performance (mean AUC – 0.96) was achieved with a combined 4-dimensional feature of all Minkowski functionals. Individually, the best feature performance was seen using curvature (mean AUC - 0.85), which characterizes the edges within a binary object. These results show that quantitative analysis of cortical bone microstructure, in a computer-aided diagnostics framework, can be used to distinguish between healthy and OI bone with high accuracy.

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Volumetric characterization of human patellar cartilage matrix on phase contrast x-ray computed tomography

Cited by 2 publications

References 32 publications

Deep transfer learning for characterizing chondrocyte patterns in phase contrast X-Ray computed tomography images of the human patellar cartilage

Deep transfer learning for characterizing chondrocyte patterns in phase contrast X-Ray computed tomography images of the human patellar cartilage

Classification of micro-CT images using 3D characterization of bone canal patterns in human osteogenesis imperfecta

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