1,819 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
Semi-supervised segmentation of ultrasound images based on patch representation and continuous min cut.
Ultrasound segmentation is a challenging problem due to the inherent speckle and some artifacts like shadows, attenuation and signal dropout. Existing methods need to include strong priors like shape priors or analytical intensity models to succeed in the segmentation. However, such priors tend to limit these methods to a specific target or imaging settings, and they are not always applicable to pathological cases. This work introduces a semi-supervised segmentation framework for ultrasound imaging that alleviates the limitation of fully automatic segmentation, that is, it is applicable to any kind of target and imaging settings. Our methodology uses a graph of image patches to represent the ultrasound image and user-assisted initialization with labels, which acts as soft priors. The segmentation problem is formulated as a continuous minimum cut problem and solved with an efficient optimization algorithm. We validate our segmentation framework on clinical ultrasound imaging (prostate, fetus, and tumors of the liver and eye). We obtain high similarity agreement with the ground truth provided by medical expert delineations in all applications (94% DICE values in average) and the proposed algorithm performs favorably with the literature
- …