An Automated Segmentation Algorithm for CT Volumes of Livers with Atypical Shapes and Large Pathological Lesions

作者：Umetsu Shun^*; Shimizu Akinobu; Watanabe Hidefumi; Kobatake Hidefumi; Nawano Shigeru

来源：IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 2014, E97D(4): 951-963.

DOI：10.1587/transinf.E97.D.951

• CT volume
• liver segmentation
• lasso
• lesion
• probabilistic atlas

This paper presents a novel liver segmentation algorithm that achieves higher performance than conventional algorithms in the segmentation of cases with unusual liver shapes and/or large liver lesions. An L1 norm was introduced to the mean squared difference to find the most relevant cases with an input case from a training dataset. A patient-specific probabilistic atlas was generated from the retrieved cases to compensate for livers with unusual shapes, which accounts for liver shape more specifically than a conventional probabilistic atlas that is averaged over a number of training cases. To make the above process robust against large pathological lesions, we incorporated a novel term based on a set of %26quot;lesion bases%26quot; proposed in this study that account for the differences from normal liver parenchyma. Subsequently, the patient-specific probabilistic atlas was forwarded to a graph-cuts-based fine segmentation step, in which a penalty function was computed from the probabilistic atlas. A leave-one-out test using clinical abdominal CT volumes was conducted to validate the performance, and proved that the proposed segmentation algorithm with the proposed patient-specific atlas reinforced by the lesion bases outperformed the conventional algorithm with a statistically significant difference.

• 出版日期2014-4