3D automatic levels propagation approach to breast MRI tumor segmentation. (1st March 2021)

• Record Type: Journal Article
• Authors: Bouchebbah, Fatah; Slimani, Hachem
• Is Part Of: Expert systems with applications. Volume 165(2021)
• Publication Date: 2021-03-01
• Subjects: Breast MRI -- Breast tumor -- 2D/3D multi-tumor segmentation -- Automatic approach -- Automatic thresholding -- Multi-view representation; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2020.113965
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: 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. Highlights: A new method named 3D Automatic Levels Propagation Approach (3D-ALPA) is proposed. 3D-ALPA is an extension of the recent 2D Levels Propagation Approach (2D-LPA) method. 3D-ALPA is automatic, supports multi-tumor, and multi-plane segmentation. 3D-ALPA has been evaluated on the two datasets 3D CMH-LIMED and RIDER. The experimental tests of 3D-ALPA have yielded very satisfying results.