TY - GEN
T1 - Pilot study of applying shape analysis to liver cirrhosis diagnosis
AU - Luo, Jie
AU - Chen, Yen Wei
AU - Han, Xian Hua
AU - Tateyama, Tomoko
AU - Furukawa, Akira
AU - Kanasaki, Shuzo
PY - 2013
Y1 - 2013
N2 - This paper explores the potential of applying shape analysis to classify normal/cirrhotic liver and in addition estimate the severity of abnormal cases. Conventional Computer-Aided Diagnosis (CAD) systems are developed for automatically providing a binary output as a second opinion to assist radiologists to draw conclusions about the condition of the pathology (normal or abnormal). After the disease is diagnosed, grasping the proceeding stage of the abnormal degree is essential for adopting the appropriate strength of treatment. However, none of existing CAD system is well established for such a challenging task. Liver cirrhosis has an important feature: morphological changes of the liver and the spleen occur during the clinical course of liver cirrhosis. In this study we constructed liver, spleen and their joint Statistical Shape Models (SSMs) to quantitatively assess the global shape variation and selected several modes from the SSMs. Then we learnt a mapping function between coefficients of selected modes and the ground truth staging label by Support Vector Regression (SVR). Using this mapping function, the proceeding stage of new input data can be estimated. Experimental results have validated the potential of our method on assisting the cirrhosis diagnosis.
AB - This paper explores the potential of applying shape analysis to classify normal/cirrhotic liver and in addition estimate the severity of abnormal cases. Conventional Computer-Aided Diagnosis (CAD) systems are developed for automatically providing a binary output as a second opinion to assist radiologists to draw conclusions about the condition of the pathology (normal or abnormal). After the disease is diagnosed, grasping the proceeding stage of the abnormal degree is essential for adopting the appropriate strength of treatment. However, none of existing CAD system is well established for such a challenging task. Liver cirrhosis has an important feature: morphological changes of the liver and the spleen occur during the clinical course of liver cirrhosis. In this study we constructed liver, spleen and their joint Statistical Shape Models (SSMs) to quantitatively assess the global shape variation and selected several modes from the SSMs. Then we learnt a mapping function between coefficients of selected modes and the ground truth staging label by Support Vector Regression (SVR). Using this mapping function, the proceeding stage of new input data can be estimated. Experimental results have validated the potential of our method on assisting the cirrhosis diagnosis.
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U2 - 10.1109/ICIP.2013.6738730
DO - 10.1109/ICIP.2013.6738730
M3 - Conference contribution
AN - SCOPUS:84897586230
SN - 9781479923410
T3 - 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
SP - 3537
EP - 3541
BT - 2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
PB - IEEE Computer Society
T2 - 2013 20th IEEE International Conference on Image Processing, ICIP 2013
Y2 - 15 September 2013 through 18 September 2013
ER -