3D automatic levels propagation approach to breast MRI tumor segmentation

Magnetic Resonance Imaging MRI is a relevant tool for breast cancer screening. Moreover, an accurate 3D segmentation of breast tumors from MRI scans plays a key role in the analysis of the disease. In this manuscript, we propose a novel 3D automatic method for segmenting MRI breast tumors, called 3D Automatic Levels Propagation Approach (3D-ALPA). The proposed method performs the segmentation automatically in two steps: in the first step, the entire MRI volume to process is segmented slice by slice. Specifically, using a new automatic approach called 2D Automatic Levels Propagation Approach (2D-ALPA) which is an improved version of a previous semi-automatic approach, named 2D Levels Propagation Approach (2D-LPA). In the second step, the partial segmentations obtained after the application of 2D-ALPA are recombined to rebuild the complete volume(s) of tumor(s). 3D-ALPA has many characteristics, mainly: it is an automatic method which can take into consideration multi-tumor segmentation, and it has the property to be easily applicable according to the Axial, Coronal, as well as Sagittal planes. Therefore, it offers a multi-view representation of the segmented tumor(s). To validate the new 3D-ALPA method, we have firstly performed tests on a 2D private dataset composed of eighteen patients to estimate the accuracy of the new 2D-ALPA in comparison to the previous 2D-LPA. The obtained results have been in favor of the proposed 2D-ALPA, showing hence an improvement in accuracy after integrating the automatization in the 2D-ALPA approach. Then, we have evaluated the complete 3D-ALPA method on a 3D private dataset constituted of MRI exams of twenty-two patients having real breast tumors of different types, and on the public RIDER dataset. Essentially, 3D-ALPA has been evaluated regarding two main features: segmentation accuracy and running time, by considering two kinds of breast tumors: non enhanced and enhanced tumors. The experimental studies have shown that 3D-ALPA has produced better results for the both kinds of tumors than a recent and concurrent method in the literature that addresses the same problematic.

Automated summary

MRI is a relevant tool for breast cancer screening, and accurate 3D segmentation of breast tumors from MRI scans is essential in disease analysis. The proposed 3D Automatic Levels Propagation Approach (3D-ALPA) automatically segments MRI breast tumors, showing improved accuracy and efficiency compared to previous methods. Additionally, 3D-ALPA offers multi-view representation and has been proven to produce better results for both non-enhanced and enhanced breast tumors.