Mr. Chien-Chun Yang Profile

Chien-Chun Yang

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Publications (6)

Proceedings Article | 13 March 2014 Paper

Predicting the biomechanical strength of proximal femur specimens with Minkowski functionals and support vector regression

Chien-Chun Yang, Mahesh Nagarajan, Markus Huber, Julio Carballido-Gamio, Jan Bauer, Thomas Baum, Felix Eckstein, Eva-Maria Lochmüller, Thomas Link, Axel Wismüller

Proceedings Volume 9038, 903810 (2014) https://doi.org/10.1117/12.2041782

KEYWORDS: Bone, Head, Computed tomography, Machine learning, Feature extraction, Biomedical optics, Radiology, Biological research, Failure analysis, 3D image processing

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Proceedings Article | 13 March 2014 Paper

Method and device for intraoperative imaging of lumpectomy specimens to provide feedback to breast surgeon for prompt re-excision during the same procedure

Andrzej Krol, Susan Hemingway, Kara Kort, Gustavo de la Rosa, Ravi Adhikary, Deepa Masrani, David Feiglin, Avice O’Connell, Mahesh Nagarajan, Chien-Chun Yang, Axel Wismüller

Proceedings Volume 9038, 90381G (2014) https://doi.org/10.1117/12.2043919

KEYWORDS: Breast, Tissues, Surgery, Digital breast tomosynthesis, Optical spheres, Imaging devices, Foam, Stereoscopy, Mammography, Tumors

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Breast conserving therapy (BCT) of breast cancer is now widely accepted due to improved cosmetic outcome and improved patients’ quality of life. One of the critical issues in performing breast-conserving surgery is trying to achieve microscopically clear surgical margins while maintaining excellent cosmesis. Unfortunately, unacceptably close or positive surgical margins occur in at least 20-25% of all patients undergoing BCT requiring repeat surgical excision days or weeks later, as permanent histopathology routinely takes days to complete. Our aim is to develop a better method for intraoperative imaging of non-palpable breast malignancies excised by wire or needle localization. Providing non-deformed three dimensional imaging of the excised breast tissue should allow more accurate assessment of tumor margins and consequently allow further excision at the time of initial surgery thus limiting the enormous financial and emotional burden of additional surgery. We have designed and constructed a device that allows preservation of the excised breast tissue in its natural anatomic position relative to the breast as it is imaged to assess adequate excision. We performed initial tests with needle-guided lumpectomy specimens using micro-CT and digital breast tomosynthesis (DBT). Our device consists of a plastic sphere inside a cylindrical holder. The surgeon inserts a freshly excised piece of breast tissue into the sphere and matches its anatomic orientation with the fiducial markers on the sphere. A custom-shaped foam is placed inside the sphere to prevent specimen deformation due to gravity. DBT followed by micro-CT images of the specimen were obtained. We confirmed that our device preserved spatial orientation of the excised breast tissue and that the location error was lower than 10mm and 10 degrees. The initial obtained results indicate that breast lesions containing microcalcifications allow a good 3D imaging of margins providing immediate intraoperative feedback for further excision as needed at the initial operation.

SPIE Journal Paper | 4 February 2014

Improving bone strength prediction in human proximal femur specimens through geometrical characterization of trabecular bone microarchitecture and support vector regression

Chien-Chun Yang, Mahesh Nagarajan, Markus Huber, Julio Carballido-Gamio, Jan Bauer, Thomas Baum, Felix Eckstein, Eva-Maria Lochmüller, Sharmila Majumdar, Thomas Link, Axel Wismüller

JEI, Vol. 23, Issue 01, 013013, (February 2014) https://doi.org/10.1117/12.10.1117/1.JEI.23.1.013013

KEYWORDS: Bone, Dual energy x-ray absorptiometry, Head, Feature extraction, Neck, Machine learning, Computed tomography, Tissues, Minerals, Performance modeling

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Proceedings Article | 29 March 2013 Paper

Predicting the biomechanical strength of proximal femur specimens through high dimensional geometric features and support vector regression

Chien-Chun Yang, Mahesh Nagarajan, Markus Huber, Julio Carballido-Gamio, Jan Bauer, Thomas Baum, Felix Eckstein, Eva Lochmüller, Sharmila Majumdar, Thomas Link, Axel Wismüller

Proceedings Volume 8672, 86721K (2013) https://doi.org/10.1117/12.2006265

KEYWORDS: Bone, Biological research, Head, Biomedical optics, Failure analysis, Machine learning, Feature extraction, Radiology, Computed tomography, Minerals

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Proceedings Article | 16 April 2012 Paper

Characterization of healthy and osteoarthritic chondrocyte cell patterns on phase contrast CT images of the knee cartilage matrix

Mahesh Nagarajan, Paola Coan, Markus Huber, Chien-Chun Yang, Christian Glaser, Maximilian Reiser, Axel Wismüller

Proceedings Volume 8317, 831720 (2012) https://doi.org/10.1117/12.910919

KEYWORDS: Cartilage, Feature extraction, Computed tomography, Machine learning, Visualization, Fuzzy logic, Statistical analysis, Phase contrast, Tissues, Image classification

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Proceedings Article | 23 February 2012 Paper

Predicting the biomechanical strength of proximal femur specimens with bone mineral density features and support vector regression

Markus Huber, Chien-Chun Yang, Julio Carballido-Gamio, Jan Bauer, Thomas Baum, Mahesh Nagarajan, Felix Eckstein, Eva Lochmüller, Sharmila Majumdar, Thomas Link, Axel Wismüller

Proceedings Volume 8315, 83151W (2012) https://doi.org/10.1117/12.911402

KEYWORDS: Bone, Minerals, Head, Feature extraction, Computing systems, Computed tomography, Computer aided diagnosis and therapy, Failure analysis, Statistical analysis, Image segmentation

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To improve the clinical assessment of osteoporotic hip fracture risk, recent computer-aided diagnosis systems explore new approaches to estimate the local trabecular bone quality beyond bone density alone to predict femoral bone strength. In this context, statistical bone mineral density (BMD) features extracted from multi-detector computed tomography (MDCT) images of proximal femur specimens and different function approximations methods were compared in their ability to predict the biomechanical strength. MDCT scans were acquired in 146 proximal femur specimens harvested from human cadavers. The femurs' failure load (FL) was determined through biomechanical testing. An automated volume of interest (VOI)-fitting algorithm was used to define a consistent volume in the femoral head of each specimen. In these VOIs, the trabecular bone was represented by statistical moments of the BMD distribution and by pairwise spatial occurrence of BMD values using the gray-level co-occurrence (GLCM) approach. A linear multi-regression analysis (MultiReg) and a support vector regression algorithm with a linear kernel (SVRlin) were used to predict the FL from the image feature sets. The prediction performance was measured by the root mean square error (RMSE) for each image feature on independent test sets; in addition the coefficient of determination R² was calculated. The best prediction result was obtained with a GLCM feature set using SVRlin, which had the lowest prediction error (RSME = 1.040±0.143, R² = 0.544) and which was significantly lower that the standard approach of using BMD.mean and MultiReg (RSME = 1.093±0.133, R² = 0.490, p<0.0001). The combined sets including BMD.mean and GLCM features had a similar or slightly lower performance than using only GLCM features. The results indicate that the performance of high-dimensional BMD features extracted from MDCT images in predicting the biomechanical strength of proximal femur specimens can be significantly improved by using support vector regression.

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