24,316 research outputs found
Task adapted reconstruction for inverse problems
The paper considers the problem of performing a task defined on a model
parameter that is only observed indirectly through noisy data in an ill-posed
inverse problem. A key aspect is to formalize the steps of reconstruction and
task as appropriate estimators (non-randomized decision rules) in statistical
estimation problems. The implementation makes use of (deep) neural networks to
provide a differentiable parametrization of the family of estimators for both
steps. These networks are combined and jointly trained against suitable
supervised training data in order to minimize a joint differentiable loss
function, resulting in an end-to-end task adapted reconstruction method. The
suggested framework is generic, yet adaptable, with a plug-and-play structure
for adjusting both the inverse problem and the task at hand. More precisely,
the data model (forward operator and statistical model of the noise) associated
with the inverse problem is exchangeable, e.g., by using neural network
architecture given by a learned iterative method. Furthermore, any task that is
encodable as a trainable neural network can be used. The approach is
demonstrated on joint tomographic image reconstruction, classification and
joint tomographic image reconstruction segmentation
New Descriptor for Glomerulus Detection in Kidney Microscopy Image
Glomerulus detection is a key step in histopathological evaluation of
microscopy images of kidneys. However, the task of automatic detection of
glomeruli poses challenges due to the disparity in sizes and shapes of
glomeruli in renal sections. Moreover, extensive variations of their
intensities due to heterogeneity in immunohistochemistry staining are also
encountered. Despite being widely recognized as a powerful descriptor for
general object detection, the rectangular histogram of oriented gradients
(Rectangular HOG) suffers from many false positives due to the aforementioned
difficulties in the context of glomerulus detection.
A new descriptor referred to as Segmental HOG is developed to perform a
comprehensive detection of hundreds of glomeruli in images of whole kidney
sections. The new descriptor possesses flexible blocks that can be adaptively
fitted to input images to acquire robustness to deformations of glomeruli.
Moreover, the novel segmentation technique employed herewith generates high
quality segmentation outputs and the algorithm is assured to converge to an
optimal solution. Consequently, experiments using real world image data reveal
that Segmental HOG achieves significant improvements in detection performance
compared to Rectangular HOG.
The proposed descriptor and method for glomeruli detection present promising
results and is expected to be useful in pathological evaluation
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Joint Image Reconstruction and Segmentation Using the Potts Model
We propose a new algorithmic approach to the non-smooth and non-convex Potts
problem (also called piecewise-constant Mumford-Shah problem) for inverse
imaging problems. We derive a suitable splitting into specific subproblems that
can all be solved efficiently. Our method does not require a priori knowledge
on the gray levels nor on the number of segments of the reconstruction.
Further, it avoids anisotropic artifacts such as geometric staircasing. We
demonstrate the suitability of our method for joint image reconstruction and
segmentation. We focus on Radon data, where we in particular consider limited
data situations. For instance, our method is able to recover all segments of
the Shepp-Logan phantom from angular views only. We illustrate the
practical applicability on a real PET dataset. As further applications, we
consider spherical Radon data as well as blurred data
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