3D Shape-Weighted Level Set Method for Breast MRI 3D Tumor Segmentation

Abstract EN

Three-dimensional (3D) medical image segmentation is used to segment the target (a lesion or an organ) in 3D medical images.

Through this process, 3D target information is obtained; hence, this technology is an important auxiliary tool for medical diagnosis.

Although some methods have proved to be successful for two-dimensional (2D) image segmentation, their direct use in the 3D case has been unsatisfactory.

To obtain more precise tumor segmentation results from 3D MR images, in this paper, we propose a method known as the 3D shape-weighted level set method (3D-SLSM).

The proposed method first converts the LSM, which is superior with respect to 2D image segmentation, into a 3D algorithm that is suitable for overall calculations in 3D image models, and which improves the efficiency and accuracy of calculations.

A 3D shape-weighted value is then added for each 3D-SLSM iterative process according to the changes in volume.

Besides increasing the convergence rate and eliminating background noise, this shape-weighted value also brings the segmented contour closer to the actual tumor margins.

To perform a quantitative analysis of 3D-SLSM and to examine its feasibility in clinical applications, we have divided our experiments into computer-simulated sequence images and actual breast MRI cases.

Subsequently, we simultaneously compared various existing 3D segmentation methods.

The experimental results demonstrated that 3D-SLSM exhibited precise segmentation results for both types of experimental images.

In addition, 3D-SLSM showed better results for quantitative data compared with existing 3D segmentation methods.