2,468 research outputs found
MIS-FM: 3D Medical Image Segmentation using Foundation Models Pretrained on a Large-Scale Unannotated Dataset
Pretraining with large-scale 3D volumes has a potential for improving the
segmentation performance on a target medical image dataset where the training
images and annotations are limited. Due to the high cost of acquiring
pixel-level segmentation annotations on the large-scale pretraining dataset,
pretraining with unannotated images is highly desirable. In this work, we
propose a novel self-supervised learning strategy named Volume Fusion (VF) for
pretraining 3D segmentation models. It fuses several random patches from a
foreground sub-volume to a background sub-volume based on a predefined set of
discrete fusion coefficients, and forces the model to predict the fusion
coefficient of each voxel, which is formulated as a self-supervised
segmentation task without manual annotations. Additionally, we propose a novel
network architecture based on parallel convolution and transformer blocks that
is suitable to be transferred to different downstream segmentation tasks with
various scales of organs and lesions. The proposed model was pretrained with
110k unannotated 3D CT volumes, and experiments with different downstream
segmentation targets including head and neck organs, thoracic/abdominal organs
showed that our pretrained model largely outperformed training from scratch and
several state-of-the-art self-supervised training methods and segmentation
models. The code and pretrained model are available at
https://github.com/openmedlab/MIS-FM.Comment: 13 pages, 8 figure
Patient-specific anisotropic model of human trunk based on MR data
There are many ways to generate geometrical models for numerical simulation, and most of them start with a segmentation step to extract the boundaries of the regions of interest. This paper presents an algorithm to generate a patient-specific three-dimensional geometric model, based on a tetrahedral mesh, without an initial extraction of contours from the volumetric data. Using the information directly available in the data, such as gray levels, we built a metric to drive a mesh adaptation process. The metric is used to specify the size and orientation of the tetrahedral elements everywhere in the mesh. Our method, which produces anisotropic meshes, gives good results with synthetic and real MRI data. The resulting model quality has been evaluated qualitatively and quantitatively by comparing it with an analytical solution and with a segmentation made by an expert. Results show that our method gives, in 90% of the cases, as good or better meshes as a similar isotropic method, based on the accuracy of the volume reconstruction for a given mesh size. Moreover, a comparison of the Hausdorff distances between adapted meshes of both methods and ground-truth volumes shows that our method decreases reconstruction errors faster. Copyright © 2015 John Wiley & Sons, Ltd.Natural
Sciences and Engineering Research Council (NSERC) of Canada and the MEDITIS training program (´Ecole Polytechnique de Montreal and NSERC)
CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks
The unprecedented increase in the usage of computer vision technology in
society goes hand in hand with an increased concern in data privacy. In many
real-world scenarios like people tracking or action recognition, it is
important to be able to process the data while taking careful consideration in
protecting people's identity. We propose and develop CIAGAN, a model for image
and video anonymization based on conditional generative adversarial networks.
Our model is able to remove the identifying characteristics of faces and bodies
while producing high-quality images and videos that can be used for any
computer vision task, such as detection or tracking. Unlike previous methods,
we have full control over the de-identification (anonymization) procedure,
ensuring both anonymization as well as diversity. We compare our method to
several baselines and achieve state-of-the-art results.Comment: CVPR 202
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