no code implementations • 31 Jan 2017 • Holger R. Roth, Le Lu, Nathan Lay, Adam P. Harrison, Amal Farag, Andrew Sohn, Ronald M. Summers
Accurate and automatic organ segmentation from 3D radiological scans is an important yet challenging problem for medical image analysis.
Ranked #1 on 3D Medical Imaging Segmentation on TCIA Pancreas-CT
no code implementations • 24 Jun 2016 • Holger R. Roth, Le Lu, Amal Farag, Andrew Sohn, Ronald M. Summers
Accurate automatic organ segmentation is an important yet challenging problem for medical image analysis.
no code implementations • 22 Jun 2015 • Holger R. Roth, Le Lu, Amal Farag, Hoo-chang Shin, Jiamin Liu, Evrim Turkbey, Ronald M. Summers
We propose and evaluate several variations of deep ConvNets in the context of hierarchical, coarse-to-fine classification on image patches and regions, i. e. superpixels.
no code implementations • 22 May 2015 • Amal Farag, Le Lu, Holger R. Roth, Jiamin Liu, Evrim Turkbey, Ronald M. Summers
We present a bottom-up approach for pancreas segmentation in abdominal CT scans that is based on a hierarchy of information propagation by classifying image patches at different resolutions; and cascading superpixels.
no code implementations • 15 Apr 2015 • Holger R. Roth, Amal Farag, Le Lu, Evrim B. Turkbey, Ronald M. Summers
We evaluate our method on CT images of 82 patients (60 for training, 2 for validation, and 20 for testing).
no code implementations • 31 Jul 2014 • Amal Farag, Le Lu, Evrim Turkbey, Jiamin Liu, Ronald M. Summers
Organ segmentation is a prerequisite for a computer-aided diagnosis (CAD) system to detect pathologies and perform quantitative analysis.