no code implementations • 5 May 2020 • Si-Qi Liu, Bogdan Georgescu, Zhoubing Xu, Youngjin Yoo, Guillaume Chabin, Shikha Chaganti, Sasa Grbic, Sebastian Piat, Brian Teixeira, Abishek Balachandran, Vishwanath RS, Thomas Re, Dorin Comaniciu
Additionally, we leverage location priors derived from manually labeled COVID-19 chest CTs patients to generate appropriate abnormality distributions.
no code implementations • 2 Apr 2020 • Shikha Chaganti, Abishek Balachandran, Guillaume Chabin, Stuart Cohen, Thomas Flohr, Bogdan Georgescu, Philippe Grenier, Sasa Grbic, Si-Qi Liu, François Mellot, Nicolas Murray, Savvas Nicolaou, William Parker, Thomas Re, Pina Sanelli, Alexander W. Sauter, Zhoubing Xu, Youngjin Yoo, Valentin Ziebandt, Dorin Comaniciu
Automated processing time to compute the severity scores was 10 seconds per case compared to 30 minutes required for manual annotations.
In this paper, we propose a framework for liver vessel morphology reconstruction using both a fully convolutional neural network and a graph attention network.
2 code implementations • 28 Mar 2019 • Yuankai Huo, Zhoubing Xu, Yunxi Xiong, Katherine Aboud, Prasanna Parvathaneni, Shunxing Bao, Camilo Bermudez, Susan M. Resnick, Laurie E. Cutting, Bennett A. Landman
To address the first challenge, multiple spatially distributed networks were used in the SLANT method, in which each network learned contextual information for a fixed spatial location.
Synthesizing the objects of interests, such as lung nodules, in medical images based on the distribution of annotated datasets can be helpful for improving the supervised learning tasks, especially when the datasets are limited by size and class balance.
The performance of medical image analysis systems is constrained by the quantity of high-quality image annotations.
no code implementations • 9 Nov 2018 • Yuankai Huo, Zhoubing Xu, Shunxing Bao, Camilo Bermudez, Hyeonsoo Moon, Prasanna Parvathaneni, Tamara K. Moyo, Michael R. Savona, Albert Assad, Richard G. Abramson, Bennett A. Landman
A clinically acquired cohort containing both T1-weighted (T1w) and T2-weighted (T2w) MRI splenomegaly scans was used to train and evaluate the performance of multi-atlas segmentation (MAS), 2D DCNN networks, and a 3D DCNN network.
SynSeg-Net is trained by using (1) unpaired intensity images from source and target modalities, and (2) manual labels only from source modality.
Whole brain segmentation on a structural magnetic resonance imaging (MRI) is essential in non-invasive investigation for neuroanatomy.
However, this is a challenging problem given not only the inherent difficulties from the ultrasound modality, e. g., low contrast and large variations, but also the heterogeneity across tasks, i. e., one classification task for all views, and then one landmark detection task for each relevant view.
no code implementations • 14 Apr 2018 • Saeid Asgari Taghanaki, Aicha Bentaieb, Anmol Sharma, S. Kevin Zhou, Yefeng Zheng, Bogdan Georgescu, Puneet Sharma, Sasa Grbic, Zhoubing Xu, Dorin Comaniciu, Ghassan Hamarneh
Skip connections in deep networks have improved both segmentation and classification performance by facilitating the training of deeper network architectures, and reducing the risks for vanishing gradients.
Herein, we proposed a novel end-to-end synthesis and segmentation network (EssNet) to achieve the unpaired MRI to CT image synthesis and CT splenomegaly segmentation simultaneously without using manual labels on CT.
However, variations in both size and shape of the spleen on MRI images may result in large false positive and false negative labeling when deploying DCNN based methods.
no code implementations • 17 May 2017 • Dong Yang, Tao Xiong, Daguang Xu, Qiangui Huang, David Liu, S. Kevin Zhou, Zhoubing Xu, Jin-Hyeong Park, Mingqing Chen, Trac. D. Tran, Sang Peter Chin, Dimitris Metaxas, Dorin Comaniciu
In this paper, we propose an automatic and fast algorithm to localize and label the vertebra centroids in 3D CT volumes.