Automated Unsupervised Segmentation of Liver Lesions in CT scans via Cahn-Hilliard Phase Separation
This addresses the challenge of accurate liver lesion segmentation for cancer detection and monitoring, though it appears incremental as it applies an existing phase separation approach to this specific medical imaging task.
The paper tackles the problem of automated liver lesion segmentation in CT scans by proposing an unsupervised 3D method using the Cahn-Hilliard equation for phase separation, which simplifies lesion detection and segmentation via thresholding, and it was tested on the 3Dircadb and LITS datasets.
The segmentation of liver lesions is crucial for detection, diagnosis and monitoring progression of liver cancer. However, design of accurate automated methods remains challenging due to high noise in CT scans, low contrast between liver and lesions, as well as large lesion variability. We propose a 3D automatic, unsupervised method for liver lesions segmentation using a phase separation approach. It is assumed that liver is a mixture of two phases: healthy liver and lesions, represented by different image intensities polluted by noise. The Cahn-Hilliard equation is used to remove the noise and separate the mixture into two distinct phases with well-defined interfaces. This simplifies the lesion detection and segmentation task drastically and enables to segment liver lesions by thresholding the Cahn-Hilliard solution. The method was tested on 3Dircadb and LITS dataset.