Models of breast lesions based on three-dimensional X-ray breast images

• Published in: Physica medica 2019-01, Vol.57, p.80-87
• Main Authors: Dukov, Nikolay, Bliznakova, Kristina, Feradov, Firgan, Buliev, Ivan, Bosmans, Hilde, Mettivier, Giovanni, Russo, Paolo, Cockmartin, Lesley, Bliznakov, Zhivko
• Format: Article
• Language: English
• Subjects: Breast lesions; Breast tomosynthesis; Dice similarity coefficients; Segmentation
• ISSN: 1120-1797; 1724-191X
• DOI: 10.1016/j.ejmp.2018.12.012

•Computational models of breast lesions with irregular shapes.•Breast tomosynthesis and CT images to extract tumor shapes.•Realistic in shape segmented lesions as evaluated by radiologists.•User friendly software tool implemented in MATLAB facilitates the tumor delineation.•Creation of a database with segmented lesions. This paper presents a method for creation of computational models of breast lesions with irregular shapes from patient Digital Breast Tomosynthesis (DBT) images or breast cadavers and whole-body Computed Tomography (CT) images. The approach includes six basic steps: (a) normalization of the intensity of the tomographic images; (b) image noise reduction; (c) binarization of the lesion area, (d) application of morphological operations to further decrease the level of artefacts; (e) application of a region growing technique to segment the lesion; and (f) creation of a final 3D lesion model. The algorithm is semi-automatic as the initial selection of the region of the lesion and the seeds for the region growing are done interactively. A software tool, performing all of the required steps, was developed in MATLAB. The method was tested and evaluated by analysing anonymized sets of DBT patient images diagnosed with lesions. Experienced radiologists evaluated the segmentation of the tumours in the slices and the obtained 3D lesion shapes. They concluded for a quite satisfactory delineation of the lesions. In addition, for three DBT cases, a delineation of the tumours was performed independently by the radiologists. In all cases the abnormality volumes segmented by the proposed algorithm were smaller than those outlined by the experts. The calculated Dice similarity coefficients for algorithm-radiologist and radiologist-radiologist showed similar values. Another selected tumour case was introduced into a computational breast model to recursively assess the algorithm. The relative volume difference between the ground-truth tumour volume and the one obtained by applying the algorithm on the synthetic volume from the virtual DBT study is 5% which demonstrates the satisfactory performance of the proposed segmentation algorithm. The software tool we developed was used to create models of different breast abnormalities, which were then stored in a database for use by researchers working in this field.