Fast semi-automatic segmentation of focal liver lesions in contrast-enhanced ultrasound, based on a probabilistic model

Assessment of focal liver lesions (FLLs) in contrast-enhanced ultrasound requires the delineation of the FLL in at least one frame of the acquired data, which is currently performed manually by experienced radiologists. Such a task leads to subjective results, is time-consuming and prone to misinterpretation and human error. This paper describes an attempt to improve this clinical practice by proposing a novel fast two-step method to automate the FLL segmentation, initialised only by a single seed point. Firstly, rectangular force functions are used to improve the accuracy and computational efficiency of an active ellipse model for approximating the FLL shape. Then, a novel probabilistic boundary refinement method is used to iteratively classify boundary pixels rapidly. The proposed method allows for faster and easier assessment of FLLs, whilst requiring less interaction, but producing results comparably consistent with manual delineations, and hence increasing the confidence of radiologists when making a diagnosis. Quantitative evaluation based on real clinical data, from two different European countries reflecting true clinical practice, demonstrates the value of the proposed method.