5,136 research outputs found
A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation
Cellular electron cryo-tomography enables the 3D visualization of cellular
organization in the near-native state and at submolecular resolution. However,
the contents of cellular tomograms are often complex, making it difficult to
automatically isolate different in situ cellular components. In this paper, we
propose a convolutional autoencoder-based unsupervised approach to provide a
coarse grouping of 3D small subvolumes extracted from tomograms. We demonstrate
that the autoencoder can be used for efficient and coarse characterization of
features of macromolecular complexes and surfaces, such as membranes. In
addition, the autoencoder can be used to detect non-cellular features related
to sample preparation and data collection, such as carbon edges from the grid
and tomogram boundaries. The autoencoder is also able to detect patterns that
may indicate spatial interactions between cellular components. Furthermore, we
demonstrate that our autoencoder can be used for weakly supervised semantic
segmentation of cellular components, requiring a very small amount of manual
annotation.Comment: Accepted by Journal of Structural Biolog
Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow
We propose a method to classify cardiac pathology based on a novel approach
to extract image derived features to characterize the shape and motion of the
heart. An original semi-supervised learning procedure, which makes efficient
use of a large amount of non-segmented images and a small amount of images
segmented manually by experts, is developed to generate pixel-wise apparent
flow between two time points of a 2D+t cine MRI image sequence. Combining the
apparent flow maps and cardiac segmentation masks, we obtain a local apparent
flow corresponding to the 2D motion of myocardium and ventricular cavities.
This leads to the generation of time series of the radius and thickness of
myocardial segments to represent cardiac motion. These time series of motion
features are reliable and explainable characteristics of pathological cardiac
motion. Furthermore, they are combined with shape-related features to classify
cardiac pathologies. Using only nine feature values as input, we propose an
explainable, simple and flexible model for pathology classification. On ACDC
training set and testing set, the model achieves 95% and 94% respectively as
classification accuracy. Its performance is hence comparable to that of the
state-of-the-art. Comparison with various other models is performed to outline
some advantages of our model
Unsupervised level set parameterization using multi-scale filtering
This paper presents a novel framework for unsupervised level set parameterization using multi-scale filtering. A standard multi-scale, directional filtering algorithm is used in order to capture the orientation coherence in edge regions. The latter is encoded in entropy-based image `heatmaps', which are able to weight forces guiding level set evolution. Experiments are conducted on two large benchmark databases as well as on real proteomics images. The experimental results demonstrate that the proposed framework is capable of accelerating contour convergence, whereas it obtains a segmentation quality comparable to the one obtained with empirically optimized parameterization
Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation
Medical image segmentation is a fundamental and critical step in many
image-guided clinical approaches. Recent success of deep learning-based
segmentation methods usually relies on a large amount of labeled data, which is
particularly difficult and costly to obtain especially in the medical imaging
domain where only experts can provide reliable and accurate annotations.
Semi-supervised learning has emerged as an appealing strategy and been widely
applied to medical image segmentation tasks to train deep models with limited
annotations. In this paper, we present a comprehensive review of recently
proposed semi-supervised learning methods for medical image segmentation and
summarized both the technical novelties and empirical results. Furthermore, we
analyze and discuss the limitations and several unsolved problems of existing
approaches. We hope this review could inspire the research community to explore
solutions for this challenge and further promote the developments in medical
image segmentation field
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Deep learning for cardiac image segmentation: A review
Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research
BRUL\`E: Barycenter-Regularized Unsupervised Landmark Extraction
Unsupervised retrieval of image features is vital for many computer vision
tasks where the annotation is missing or scarce. In this work, we propose a new
unsupervised approach to detect the landmarks in images, validating it on the
popular task of human face key-points extraction. The method is based on the
idea of auto-encoding the wanted landmarks in the latent space while discarding
the non-essential information (and effectively preserving the
interpretability). The interpretable latent space representation (the
bottleneck containing nothing but the wanted key-points) is achieved by a new
two-step regularization approach. The first regularization step evaluates
transport distance from a given set of landmarks to some average value (the
barycenter by Wasserstein distance). The second regularization step controls
deviations from the barycenter by applying random geometric deformations
synchronously to the initial image and to the encoded landmarks. We demonstrate
the effectiveness of the approach both in unsupervised and semi-supervised
training scenarios using 300-W, CelebA, and MAFL datasets. The proposed
regularization paradigm is shown to prevent overfitting, and the detection
quality is shown to improve beyond the state-of-the-art face models.Comment: 10 main pages with 6 figures and 1 Table, 14 pages total with 6
supplementary figures. I.B. and N.B. contributed equally. D.V.D. is
corresponding autho
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