A Framework for Fusion of T1-Weighted and Dynamic MRI Sequences

• Type: Article in International Conference Proceedings Book
• Date: 2020

Authors

Teixeira, JF (Author) Other; Bessa, S (Author) Other; Gouveia, PF (Author) Other; Oliveira, HP (Author) FCUP

Conference proceedings International

Title: Image Analysis and Recognition - 17th International Conference, ICIAR 2020, Póvoa de Varzim, Portugal, June 24-26, 2020, Proceedings, Part II

Pages: 157-169

17th International Conference on Image Analysis and Recognition, ICIAR 2020

24 June 2020 through 26 June 2020

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-00S-B27

DOI: 10.1007/978-3-030-50516-5_14

Abstract (EN): Breast cancer imaging research has seen continuous progress throughout the years. Innovative visualization tools and easier planning techniques are being developed. Image segmentation methodologies generally have best results when applied to specific types of exams or sequences, as their features enhance and expedite those approaches. Particular methods have more purchase with the segmentation of particular structures. This is the case with diverse breast structures and the respective lesions on MRI sequences, over T1w and Dyn. The present study presents a methodology to tackle an unapproached task. We aim to facilitate the volumetric alignment of data retrieved from T1w and Dyn sequences, leveraging breast surface segmentation and registration. The proposed method revolves around Canny edge detection and mending potential holes on the surface, in order to accurately reproduce the breast shape. The contour is refined with a Level-set approach and the surfaces are aligned together using a restriction of the Iterative Closest Point (ICP) method. This could easily be applied to other paired same-time, volumetric sequences. The process seems to have promising results as average two-dimensional contour distances are at sub-voxel resolution and visual results seem well within range for the valid transference of other segmented or annotated structures. © Springer Nature Switzerland AG 2020.

Language: English

Type (Professor's evaluation): Scientific