Shape Detection In 2D Ultrasound Images
release_ek56tdhcavdcbo3ok5sjjofq4q
by
Ruturaj Gole, Haixia Wu, Subho Ghose
2019
Abstract
Ultrasound images are one of the most widely used techniques in clinical
settings to analyze and detect different organs for study or diagnoses of
diseases. The dependence on subjective opinions of experts such as radiologists
calls for an automatic recognition and detection system that can provide an
objective analysis. Previous work done on this topic is limited and can be
classified by the organ of interest. Hybrid neural networks, linear and
logistic regression models, 3D reconstructed models, and various machine
learning techniques have been used to solve complex problems such as detection
of lesions and cancer. Our project aims to use Dual Path Networks (DPN) to
segment and detect shapes in ultrasound images taken from 3D printed models of
the liver. Further the DPN deep architectures could be coupled with Fully
Convolutional Network (FCN) to refine the results. Data denoised with various
filters would be used to gauge how they fare against each other and provide the
best results. Small amount of dataset works with DPNs, and hence, that should
be appropriate for us as our dataset shall be limited in size. Moreover, the
ultrasound scans shall need to be taken from different orientations of the
scanner with respect to the organ, such that the training dataset can
accurately perform segmentation and shape detection.
In text/plain
format
Archived Files and Locations
application/pdf 533.2 kB
file_nw5mcftlbrftxcvtb7me2bvk6y
|
arxiv.org (repository) web.archive.org (webarchive) |
1911.09863v1
access all versions, variants, and formats of this works (eg, pre-prints)