Abstract
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.
Highlights
In the process of breast cancer screening or medical treatment, the size and shape of a tumor is often an important basis for the diagnosis or treatment strategy
The computer-simulated images that were generated in Figure 5 will be used to carry out different 3D tumor segmentation methods, and the results will be compared after the segmentation
We see that 3D shape-weighted level set method (3D-shape-based LSM (SLSM)) has the best performance in accuracy, specificity, and false alarm rates in the experimental results (Tables 2 and 3), where image blurring was carried out using different masks (3 × 3 and 5 × 5)
Summary
In the process of breast cancer screening or medical treatment, the size and shape of a tumor is often an important basis for the diagnosis or treatment strategy. Segmenting the tumor from a medical image can improve the diagnostic accuracy of doctors, and become a guide for the surgery. Breast MRI tumor segmentation has always been a challenging task. Some methods are based on local features; they are often dependent on the suitability of the features extracted as well as the image consistency. If these methods are used in highly variable medical images, their segmentation accuracy is relatively unstable. Some segmentation methods demonstrate superior performance, such as LSM and multispectral detection technology [7] that do give good results for tumor segmentation in 2D medical images. It is noteworthy that in order to meet the needs of multispectral detection technology, different parameters (such as T1, T2, and PD) must be used to produce multispectral MRIs
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