386 research outputs found
Fully Automatic Segmentation of Lumbar Vertebrae from CT Images using Cascaded 3D Fully Convolutional Networks
We present a method to address the challenging problem of segmentation of
lumbar vertebrae from CT images acquired with varying fields of view. Our
method is based on cascaded 3D Fully Convolutional Networks (FCNs) consisting
of a localization FCN and a segmentation FCN. More specifically, in the first
step we train a regression 3D FCN (we call it "LocalizationNet") to find the
bounding box of the lumbar region. After that, a 3D U-net like FCN (we call it
"SegmentationNet") is then developed, which after training, can perform a
pixel-wise multi-class segmentation to map a cropped lumber region volumetric
data to its volume-wise labels. Evaluated on publicly available datasets, our
method achieved an average Dice coefficient of 95.77 0.81% and an average
symmetric surface distance of 0.37 0.06 mm.Comment: 5 pages and 5 figure
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
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