1,307 research outputs found
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
Atlas-Based Prostate Segmentation Using an Hybrid Registration
Purpose: This paper presents the preliminary results of a semi-automatic
method for prostate segmentation of Magnetic Resonance Images (MRI) which aims
to be incorporated in a navigation system for prostate brachytherapy. Methods:
The method is based on the registration of an anatomical atlas computed from a
population of 18 MRI exams onto a patient image. An hybrid registration
framework which couples an intensity-based registration with a robust
point-matching algorithm is used for both atlas building and atlas
registration. Results: The method has been validated on the same dataset that
the one used to construct the atlas using the "leave-one-out method". Results
gives a mean error of 3.39 mm and a standard deviation of 1.95 mm with respect
to expert segmentations. Conclusions: We think that this segmentation tool may
be a very valuable help to the clinician for routine quantitative image
exploitation.Comment: International Journal of Computer Assisted Radiology and Surgery
(2008) 000-99
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
Convolutional Neural Networks (CNNs) have been recently employed to solve
problems from both the computer vision and medical image analysis fields.
Despite their popularity, most approaches are only able to process 2D images
while most medical data used in clinical practice consists of 3D volumes. In
this work we propose an approach to 3D image segmentation based on a
volumetric, fully convolutional, neural network. Our CNN is trained end-to-end
on MRI volumes depicting prostate, and learns to predict segmentation for the
whole volume at once. We introduce a novel objective function, that we optimise
during training, based on Dice coefficient. In this way we can deal with
situations where there is a strong imbalance between the number of foreground
and background voxels. To cope with the limited number of annotated volumes
available for training, we augment the data applying random non-linear
transformations and histogram matching. We show in our experimental evaluation
that our approach achieves good performances on challenging test data while
requiring only a fraction of the processing time needed by other previous
methods
Image Registration of In Vivo Micro-Ultrasound and Ex Vivo Pseudo-Whole Mount Histopathology Images of the Prostate: A Proof-of-Concept Study
Early diagnosis of prostate cancer significantly improves a patient's 5-year
survival rate. Biopsy of small prostate cancers is improved with image-guided
biopsy. MRI-ultrasound fusion-guided biopsy is sensitive to smaller tumors but
is underutilized due to the high cost of MRI and fusion equipment.
Micro-ultrasound (micro-US), a novel high-resolution ultrasound technology,
provides a cost-effective alternative to MRI while delivering comparable
diagnostic accuracy. However, the interpretation of micro-US is challenging due
to subtle gray scale changes indicating cancer vs normal tissue. This challenge
can be addressed by training urologists with a large dataset of micro-US images
containing the ground truth cancer outlines. Such a dataset can be mapped from
surgical specimens (histopathology) onto micro-US images via image
registration. In this paper, we present a semi-automated pipeline for
registering in vivo micro-US images with ex vivo whole-mount histopathology
images. Our pipeline begins with the reconstruction of pseudo-whole-mount
histopathology images and a 3-dimensional (3D) micro-US volume. Each
pseudo-whole-mount histopathology image is then registered with the
corresponding axial micro-US slice using a two-stage approach that estimates an
affine transformation followed by a deformable transformation. We evaluated our
registration pipeline using micro-US and histopathology images from 18 patients
who underwent radical prostatectomy. The results showed a Dice coefficient of
0.94 and a landmark error of 2.7 mm, indicating the accuracy of our
registration pipeline. This proof-of-concept study demonstrates the feasibility
of accurately aligning micro-US and histopathology images. To promote
transparency and collaboration in research, we will make our code and dataset
publicly available
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