Liver Segmentation And Preoperative Assessment Based On SPECT Images

Hepatocellular carcinoma is a kind of high incidence cancer in our country. It has been a serious threat to human life and health all over the world. Surgical resection is the primary means of treatment for liver cancer. The liver function evaluation is the key reference for hepatectomy. Postoperative liver failure may occur if the preoperative evaluation is not sufficient. Single photon emission computed tomography is an imaging method which can reflect the functional hepatocytes. It is a promising way of evaluating functional liver volume and partial liver function, meanwhile, the risk of postoperative liver failure can be predicted on this basis.Currently, liver function evaluation methods in clinic are mainly for the overall function of the liver. The existing assessment methods, such as serological indicators, clinical grading systems and dynamic quantitative liver function tests, are qualitative assessment instead of quantitative evaluation. On the other hand, they can not assess partial liver function and predict liver status after surgery. Focusing on the issues, this paper carries out the study and exploration based on SPECT images.Firstly, accurate extraction and segmentation of the liver boundary. We propose an automatic liver segmentation method based on active contour models for adjacent SPECT image sequences. First, initiating the segmentation contour. In the segmentation process, regarding the segmentation result of the upper-slice as the subsequent slice initial contour until all the SPECT image sequences are segmented. Then, a knowledge-based training set for average liver shape model is used to distinguish liver from other organs such as spleen and heart.Secondly, identification and calculation for the liver function evaluation indexes based on SPECT images. Liver volume and liver function cell count can provide the difference and injury of functional liver cell; portal venous perfusion index can be influenced by liver fibrosis and hardening, as well as the change of portal vein pressure; the liver 99mTc-phytate uptake value is a reflection of the number and function of Kupffer cells, which can indicate the functional liver cells quantitative indirectly.Finally, three-dimensional visualization and simulation of hepatectomy. Based on the above segmentation results, texture mapping is contributed to the three-dimensional visualization. Meanwhile, we can simulate the liver surgery and predict the liver function after hepatectomy. Compared with the existing clinical liver function evaluation methods, our work has a promising application.In summary, the thesis establishes a framework for liver SPECT image segmentation, liver function evaluation indexes calculation and three-dimensional reconstruction. Experimental results show that the results of this study can achieve automatic segmentation for the liver boundary. Besides, it can provide more accurate evaluation and prediction for the patients, which is the foundation for computer-aided diagnosis and hepatectomy.