108 research outputs found
Compression of 4D medical image and spatial segmentation using deformable models
Ph.DDOCTOR OF PHILOSOPH
Systems and Methods for Modeling Three-Dimensional Objects From Two-Dimensional Images
In one embodiment, a system and method for modeling a three-dimensional object includes capturing two-dimensional images of the object from multiple different viewpoints to obtain multiple views of the object, estimating slices of the object that lie in parallel planes that cut through the object, and computing a surface of the object from the estimated slices
Regression Metric Loss: Learning a Semantic Representation Space for Medical Images
Regression plays an essential role in many medical imaging applications for
estimating various clinical risk or measurement scores. While training
strategies and loss functions have been studied for the deep neural networks in
medical image classification tasks, options for regression tasks are very
limited. One of the key challenges is that the high-dimensional feature
representation learned by existing popular loss functions like Mean Squared
Error or L1 loss is hard to interpret. In this paper, we propose a novel
Regression Metric Loss (RM-Loss), which endows the representation space with
the semantic meaning of the label space by finding a representation manifold
that is isometric to the label space. Experiments on two regression tasks, i.e.
coronary artery calcium score estimation and bone age assessment, show that
RM-Loss is superior to the existing popular regression losses on both
performance and interpretability. Code is available at
https://github.com/DIAL-RPI/Regression-Metric-Loss.Comment: Accepted by MICCAI202
Deeply-Supervised CNN for Prostate Segmentation
Prostate segmentation from Magnetic Resonance (MR) images plays an important
role in image guided interven- tion. However, the lack of clear boundary
specifically at the apex and base, and huge variation of shape and texture
between the images from different patients make the task very challenging. To
overcome these problems, in this paper, we propose a deeply supervised
convolutional neural network (CNN) utilizing the convolutional information to
accurately segment the prostate from MR images. The proposed model can
effectively detect the prostate region with additional deeply supervised layers
compared with other approaches. Since some information will be abandoned after
convolution, it is necessary to pass the features extracted from early stages
to later stages. The experimental results show that significant segmentation
accuracy improvement has been achieved by our proposed method compared to other
reported approaches.Comment: Due to a crucial sign error in equation
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