Liver segmentation on CT images using random walkers and fuzzy c-means for treatment planning and monitoring of tumors in liver cancer patients

Liver and liver tumor segmentation from Computed Tomography (CT) images and tumor burden analysis play an important role in the choice of therapeutic strategies for liver tumor treatment planning and monitoring. Furthermore, accurate segmentation of the blood vasculature structure can result in improved diagnosis and easier liver tumor treatment planning. Although many methods have been proposed, this segmentation remains a challenging task due to the lack of visible edges on most boundaries of the liver coupled with high variability of both intensity patterns and anatomical appearances in pathological livers. In this thesis, a new segmentation method for liver, liver tumors and liver vasculature structure from contrast-enhanced CT imaging is proposed. As manual segmentation for liver treatment planning is both labor intensive and timeconsuming, a more accurate and automatic segmentation is desired. The proposed method is fully automatic, requiring no user interaction. The proposed segmentation is evaluated on real-world clinical data using publicly accessible benchmark clinical liver datasets containing one of the highest numbers of tumors and pathological livers utilized for liver tumor and vasculature segmentation. The proposed method is based on a hybrid method integrating random walkers algorithm with integrated priors and particle swarm optimized spatial fuzzy c-means (FCM) algorithm with level set method and AdaBoost classifier. Based on the location of the lung, the liver dome is automatically detected and the liver is then extracted by random walkers method and refined using a fuzzy level set method. This is followed by the clustering of the liver tissues using particle swarm optimized spatial FCM algorithm. Then, these tissues are classified into tumors and blood vessels by an AdaBoost classification method based on tissue features extracted utilizing first, second and higher order image features selected by a minimal-redundancy maximalrelevance feature selection approach. Finally, the segmentation is refined using level set method.The proposed method is able to segment all tumors and blood vessels with a largest axial diameter of over 5mm and 3mm, respectively. In comparison, RECIST standard commonly used in evaluation of tumors suggests a minimum largest axial diameter of 10mm for tumors and has no recommendations for the minimum largest axial diameter of blood vessels. The proposed method showed high accuracy on segmenting livers with a mean overlap error of 6.3% and mean absolute relative volume difference of 1.9%. In the case of liver tumor segmentation, with a mean overlap error of 19.6% and mean absolute relative volume difference of 11.2%. For liver vasculature segmentation, a mean overlap error of 35.9% and mean absolute relative volume difference of 16.7% was achieved by using the proposed segmentation method, making it amongst the most accurate liver vasculature segmentation methods. The medical applicability of the proposed method was further validated by a consultant radiologist where in 80% of the segmented tumors, the segmentation by the proposed method was preferred over the provided ground truth segmentation in the dataset. In case of liver envelope segmentation, the consultant radiologist suggested that the liver segmentation by the proposed method is clinically acceptable.

Preview Text FK 2018 12 IR.pdf Download (1MB) | Preview

Actions (login required)

View Item