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The segmentation of liver lesions is crucial for detection, diagnosis andmonitoring progression of liver cancer. However, design of accurate automatedmethods remains challenging due to high noise in CT scans, low contrast betweenliver and lesions, as well as large lesion variability. We propose a 3Dautomatic, unsupervised method for liver lesions segmentation using a phaseseparation approach. It is assumed that liver is a mixture of two phases:healthy liver and lesions, represented by different image intensities pollutedby noise. The Cahn-Hilliard equation is used to remove the noise and separatethe mixture into two distinct phases with well-defined interfaces. Thissimplifies the lesion detection and segmentation task drastically and enablesto segment liver lesions by thresholding the Cahn-Hilliard solution. The methodwas tested on 3Dircadb and LITS dataset.

Automated Unsupervised Segmentation of Liver Lesions in CT scans via Cahn-Hilliard Phase Separation