Medical Imaging

Learning Chan-Vese - A generalization of the Chan-Vese level set image segmentation method as a Recurrent Neural Network (RNN).

• O. Akal, A. Barbu. Fast 3D Liver Segmentation Using a Trained Deep Chan-Vese Model. Electronics 11 No. 20, 3323 (link, GitHub)
• O. Akal, A. Barbu. Learning Chan-Vese. ICIP 2019. (pdf)

Robust Loss functions - Training loss functions for classification that are robust to labeling noise.

• D. Li, A. Barbu. Training a CNN for Guidewire Detection. (pdf)
• A. Barbu, L. Lu, H. Roth, A. Seff, R.M. Summers. An Analysis of Robust Cost Functions for CNN in Computer-Aided Diagnosis. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 2016. (pdf, link)

Segmentation-Based Features - Features obtained from a segmentation of the object (e.g. lymph node) that are aligned to the object shape.

• A. Barbu, M. Suehling, X. Xu, D. Liu, S. K. Zhou, D. Comaniciu. Automatic Detection and Segmentation of Lymph Nodes from CT Data. IEEE Trans Medical Imaging, 31, No. 2, 240–250, 2012.(pdf)

Marginal Space Learning - A learning-based optimization method that achieves many orders of magnitude speedup for object detection in large parameter spaces.

• A. Barbu. A Directed Graph Approach to Active Contours. ICIP 2017. (pdf)
• W. Wu, T. Chen , A. Barbu, P. Wang, N. Strobel, S. Zhou, D. Comaniciu. Learning-based Hypothesis Fusion for Robust Catheter Tracking in 2D X-ray Fluoroscopy. CVPR 2011 (pdf)
• S. Seifert, A. Barbu, S. Zhou, D. Liu, J. Feulner, M. Huber, M. Suehling, A. Cavallaro, D. Comaniciu. Hierarchical parsing and semantic navigation of full body CT data. SPIE Medical Imaging, 2009 (pdf)
• Y. Zheng, A. Barbu, B. Georgescu, M. Scheuering and D. Comaniciu. Four-Chamber Heart Modeling and Automatic Segmentation for 3D Cardiac CT Volumes Using Marginal Space Learning and Steerable Features. IEEE Trans Medical Imaging, November 2008. (pdf)
• L. Lu, A. Barbu, M. Wolf, J. Liang, M. Salganicoff, D. Comaniciu. Accurate Polyp Segmentation for 3D CT Colonography Using Multi-Staged Probabilistic Binary Learning and Compositional Model. CVPR 2008.(pdf)
• A. Barbu, V. Athitsos, B. Georgescu, S. Boehm, P. Durlak, D. Comaniciu. Hierarchical Learning of Curves: Application to Guidewire Localization in Fluoroscopy. CVPR 2007 (pdf)