3 research outputs found
Learning Deformable Point Set Registration with Regularized Dynamic Graph CNNs for Large Lung Motion in COPD Patients
Deformable registration continues to be one of the key challenges in medical
image analysis. While iconic registration methods have started to benefit from
the recent advances in medical deep learning, the same does not yet apply for
the registration of point sets, e.g. registration based on surfaces, keypoints
or landmarks. This is mainly due to the restriction of the convolution operator
in modern CNNs to densely gridded input. However, with the newly developed
methods from the field of geometric deep learning suitable tools are now
emerging, which enable powerful analysis of medical data on irregular domains.
In this work, we present a new method that enables the learning of regularized
feature descriptors with dynamic graph CNNs. By incorporating the learned
geometric features as prior probabilities into the well-established coherent
point drift (CPD) algorithm, formulated as differentiable network layer, we
establish an end-to-end framework for robust registration of two point sets.
Our approach is evaluated on the challenging task of aligning keypoints
extracted from lung CT scans in inhale and exhale states with large
deformations and without any additional intensity information. Our results
indicate that the inherent geometric structure of the extracted keypoints is
sufficient to establish descriptive point features, which yield a significantly
improved performance and robustness of our registration framework.Comment: accepted for MICCAI 2019 Workshop Graph Learning in Medical Imagin
Development and Characterization of a Chest CT Atlas
A major goal of lung cancer screening is to identify individuals with
particular phenotypes that are associated with high risk of cancer. Identifying
relevant phenotypes is complicated by the variation in body position and body
composition. In the brain, standardized coordinate systems (e.g., atlases) have
enabled separate consideration of local features from gross/global structure.
To date, no analogous standard atlas has been presented to enable spatial
mapping and harmonization in chest computational tomography (CT). In this
paper, we propose a thoracic atlas built upon a large low dose CT (LDCT)
database of lung cancer screening program. The study cohort includes 466 male
and 387 female subjects with no screening detected malignancy (age 46-79 years,
mean 64.9 years). To provide spatial mapping, we optimize a multi-stage
inter-subject non-rigid registration pipeline for the entire thoracic space. We
evaluate the optimized pipeline relative to two baselines with alternative
non-rigid registration module: the same software with default parameters and an
alternative software. We achieve a significant improvement in terms of
registration success rate based on manual QA. For the entire study cohort, the
optimized pipeline achieves a registration success rate of 91.7%. The
application validity of the developed atlas is evaluated in terms of
discriminative capability for different anatomic phenotypes, including body
mass index (BMI), chronic obstructive pulmonary disease (COPD), and coronary
artery calcification (CAC).Comment: Accepted by SPIE2021 Medical Imaging (oral
Recursive Refinement Network for Deformable Lung Registration between Exhale and Inhale CT Scans
Unsupervised learning-based medical image registration approaches have
witnessed rapid development in recent years. We propose to revisit a commonly
ignored while simple and well-established principle: recursive refinement of
deformation vector fields across scales. We introduce a recursive refinement
network (RRN) for unsupervised medical image registration, to extract
multi-scale features, construct normalized local cost correlation volume and
recursively refine volumetric deformation vector fields. RRN achieves state of
the art performance for 3D registration of expiratory-inspiratory pairs of CT
lung scans. On DirLab COPDGene dataset, RRN returns an average Target
Registration Error (TRE) of 0.83 mm, which corresponds to a 13% error reduction
from the best result presented in the leaderboard. In addition to comparison
with conventional methods, RRN leads to 89% error reduction compared to
deep-learning-based peer approaches