Prof. Jose-Antonio Pérez-Carrasco Profile

Prof. Jose-Antonio Pérez-Carrasco

Associate Professor at Univ. de Sevilla

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Proceedings Article | 10 March 2020 Paper

Multi-label segmentation of bone, muscle, and fat in CT volumes via convex relaxation

J. A. Pérez-Carrasco, B. Acha, C. Serrano

Proceedings Volume 11313, 1131331 (2020) https://doi.org/10.1117/12.2548077

KEYWORDS: Bone, Tissues, Image segmentation, Databases, Chest, Computed tomography

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The segmentation of bone, fat and muscle tissue in CT volumes is of special interest for surgeons and radiologists in order to diagnose some diseases and/or in surgery planning. These tissues are very difficult to delineate due to the presence of multiple and different structures and the similarity in terms of Hounsfield values with surrounding organs. In this paper, an automatic algorithm to implement the segmentation of bone, muscle and fat tissue is shown. The segmentation is carried out by minimizing an energy function via convex relaxation. In previous works carried out by the authors, only two labels had been considered (bone and muscle) and the methods had strong problems to segment skeletal muscle accurately due to the presence of internal organs with Hounsfield values similar to those within muscle tissue. In the present work, prior knowledge about the distribution of skeletal muscle in abdominal, chest and pelvis CT volumes has been considered by including a binary distance transform in the computation of the cost terms. Furthermore, a third label corresponding to fat tissue has been included in the segmentation. A public database has been used to assess the performance of the algorithm. Different metrics such as DICE, Jaccard, specificity, sensitivity and Positive Predictive Value (PPV) indexes have been obtained to evaluate the algorithm performance The technique has been compared with a previous multi-label scheme, a Hybrid level-set model implementation and a thresholding algorithm. The algorithm proposed outperformed the other methods in all the metrics considered.

Proceedings Article | 20 March 2015 Paper

Segmentation of bone structures in 3D CT images based on continuous max-flow optimization

J. A. Pérez-Carrasco, B. Acha-Piñero, C. Serrano

Proceedings Volume 9413, 94133Y (2015) https://doi.org/10.1117/12.2082139

KEYWORDS: Bone, Image segmentation, Image processing algorithms and systems, Computed tomography, 3D image processing, Tissues, Surgery, Cesium, 3D modeling, Optimization (mathematics)

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In this paper an algorithm to carry out the automatic segmentation of bone structures in 3D CT images has been implemented. Automatic segmentation of bone structures is of special interest for radiologists and surgeons to analyze bone diseases or to plan some surgical interventions. This task is very complicated as bones usually present intensities overlapping with those of surrounding tissues. This overlapping is mainly due to the composition of bones and to the presence of some diseases such as Osteoarthritis, Osteoporosis, etc. Moreover, segmentation of bone structures is a very time-consuming task due to the 3D essence of the bones. Usually, this segmentation is implemented manually or with algorithms using simple techniques such as thresholding and thus providing bad results. In this paper gray information and 3D statistical information have been combined to be used as input to a continuous max-flow algorithm. Twenty CT images have been tested and different coefficients have been computed to assess the performance of our implementation. Dice and Sensitivity values above 0.91 and 0.97 respectively were obtained. A comparison with Level Sets and thresholding techniques has been carried out and our results outperformed them in terms of accuracy.

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