67 research outputs found
Anatomy-guided domain adaptation for 3D in-bed human pose estimation
3D human pose estimation is a key component of clinical monitoring systems.
The clinical applicability of deep pose estimation models, however, is limited
by their poor generalization under domain shifts along with their need for
sufficient labeled training data. As a remedy, we present a novel domain
adaptation method, adapting a model from a labeled source to a shifted
unlabeled target domain. Our method comprises two complementary adaptation
strategies based on prior knowledge about human anatomy. First, we guide the
learning process in the target domain by constraining predictions to the space
of anatomically plausible poses. To this end, we embed the prior knowledge into
an anatomical loss function that penalizes asymmetric limb lengths, implausible
bone lengths, and implausible joint angles. Second, we propose to filter pseudo
labels for self-training according to their anatomical plausibility and
incorporate the concept into the Mean Teacher paradigm. We unify both
strategies in a point cloud-based framework applicable to unsupervised and
source-free domain adaptation. Evaluation is performed for in-bed pose
estimation under two adaptation scenarios, using the public SLP dataset and a
newly created dataset. Our method consistently outperforms various
state-of-the-art domain adaptation methods, surpasses the baseline model by
31%/66%, and reduces the domain gap by 65%/82%. Source code is available at
https://github.com/multimodallearning/da-3dhpe-anatomy.Comment: submitted to Medical Image Analysi
Segmentation of Retinal Low-Cost Optical Coherence Tomography Images using Deep Learning
The treatment of age-related macular degeneration (AMD) requires continuous
eye exams using optical coherence tomography (OCT). The need for treatment is
determined by the presence or change of disease-specific OCT-based biomarkers.
Therefore, the monitoring frequency has a significant influence on the success
of AMD therapy. However, the monitoring frequency of current treatment schemes
is not individually adapted to the patient and therefore often insufficient.
While a higher monitoring frequency would have a positive effect on the success
of treatment, in practice it can only be achieved with a home monitoring
solution. One of the key requirements of a home monitoring OCT system is a
computer-aided diagnosis to automatically detect and quantify pathological
changes using specific OCT-based biomarkers. In this paper, for the first time,
retinal scans of a novel self-examination low-cost full-field OCT (SELF-OCT)
are segmented using a deep learning-based approach. A convolutional neural
network (CNN) is utilized to segment the total retina as well as pigment
epithelial detachments (PED). It is shown that the CNN-based approach can
segment the retina with high accuracy, whereas the segmentation of the PED
proves to be challenging. In addition, a convolutional denoising autoencoder
(CDAE) refines the CNN prediction, which has previously learned retinal shape
information. It is shown that the CDAE refinement can correct segmentation
errors caused by artifacts in the OCT image.Comment: Accepted for SPIE Medical Imaging 2020: Computer-Aided Diagnosi
- …