10,264 research outputs found
Segmentation-by-Detection: A Cascade Network for Volumetric Medical Image Segmentation
We propose an attention mechanism for 3D medical image segmentation. The
method, named segmentation-by-detection, is a cascade of a detection module
followed by a segmentation module. The detection module enables a region of
interest to come to attention and produces a set of object region candidates
which are further used as an attention model. Rather than dealing with the
entire volume, the segmentation module distills the information from the
potential region. This scheme is an efficient solution for volumetric data as
it reduces the influence of the surrounding noise which is especially important
for medical data with low signal-to-noise ratio. Experimental results on 3D
ultrasound data of the femoral head shows superiority of the proposed method
when compared with a standard fully convolutional network like the U-Net
Volumetric Attention for 3D Medical Image Segmentation and Detection
A volumetric attention(VA) module for 3D medical image segmentation and
detection is proposed. VA attention is inspired by recent advances in video
processing, enables 2.5D networks to leverage context information along the z
direction, and allows the use of pretrained 2D detection models when training
data is limited, as is often the case for medical applications. Its integration
in the Mask R-CNN is shown to enable state-of-the-art performance on the Liver
Tumor Segmentation (LiTS) Challenge, outperforming the previous challenge
winner by 3.9 points and achieving top performance on the LiTS leader board at
the time of paper submission. Detection experiments on the DeepLesion dataset
also show that the addition of VA to existing object detectors enables a 69.1
sensitivity at 0.5 false positive per image, outperforming the best published
results by 6.6 points.Comment: Accepted by MICCAI 201
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
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