Weakly-Supervised Learning of Metric Aggregations for Deformable Image Registration

09/24/2018
by   Enzo Ferrante, et al.
2

Deformable registration has been one of the pillars of biomedical image computing. Conventional approaches refer to the definition of a similarity criterion that, once endowed with a deformation model and a smoothness constraint, determines the optimal transformation to align two given images. The definition of this metric function is among the most critical aspects of the registration process. We argue that incorporating semantic information (in the form of anatomical segmentation maps) into the registration process will further improve the accuracy of the results. In this paper, we propose a novel weakly supervised approach to learn domain specific aggregations of conventional metrics using anatomical segmentations. This combination is learned using latent structured support vector machines (LSSVM). The learned matching criterion is integrated within a metric free optimization framework based on graphical models, resulting in a multi-metric algorithm endowed with a spatially varying similarity metric function conditioned on the anatomical structures. We provide extensive evaluation on three different datasets of CT and MRI images, showing that learned multi-metric registration outperforms single-metric approaches based on conventional similarity measures.

READ FULL TEXT

page 4

page 5

page 7

research
07/19/2017

Deformable Registration through Learning of Context-Specific Metric Aggregation

We propose a novel weakly supervised discriminative algorithm for learni...
research
05/16/2022

Weakly-supervised Biomechanically-constrained CT/MRI Registration of the Spine

CT and MRI are two of the most informative modalities in spinal diagnost...
research
01/20/2020

Learning Deformable Registration of Medical Images with Anatomical Constraints

Deformable image registration is a fundamental problem in the field of m...
research
11/05/2017

Label-driven weakly-supervised learning for multimodal deformable image registration

Spatially aligning medical images from different modalities remains a ch...
research
11/03/2022

𝒳-Metric: An N-Dimensional Information-Theoretic Framework for Groupwise Registration and Deep Combined Computing

This paper presents a generic probabilistic framework for estimating the...
research
12/05/2018

Enhancing Label-Driven Deep Deformable Image Registration with Local Distance Metrics for State-of-the-Art Cardiac Motion Tracking

While deep learning has achieved significant advances in accuracy for me...
research
06/24/2023

SAM++: Enhancing Anatomic Matching using Semantic Information and Structural Inference

Medical images like CT and MRI provide detailed information about the in...

Please sign up or login with your details

Forgot password? Click here to reset