Deepak Chittajallu

Deepak Chittajallu

Principal Engineer

Deepak is a Principal R&D Engineer on the medical computing team at Kitware, where in he is involved in the development of computer vision and machine learning algorithms to solve a wide spectrum of problems in medicine and biology. Some of the key research topics that he has worked on at Kitware include: (i) quantitative analysis of histopathology images for cancer diagnosis and prognostication, (ii) 3D cross-modality vessel segmentation using deep learning, (iii) image-based methods for cardio-respiratory phase estimation, gating, and temporal super-resolution of cardiac ultrasound videos, (iv) chronic disease surveillance on social media platforms such as Twitter and Instagram, and (iv) semantic indexing and retrieval of minimally invasive surgery videos.

Deepak received a Bachelor of Technology in Computer Science and Information Technology from Jawaharlal Nehru Technological University. He went on to complete a Master of Science and a Doctor of Philosophy in Computer Science from the University of Houston. Prior to joining Kitware, he was a postdoctoral research fellow in the Laboratory of Computational Cell Biology of Harvard Medical School.

Deepak’s post-doctoral research at Harvard was in the areas of bio-image analysis and machine learning. His research involved the development of a computational framework for automated quantification of the effects of cancer drugs on cell-cycle progression at a single-cell level in human tumor models cultured in living mice from 3D intravital microscopy data. The framework employs a synergistic combination of image analysis and machine learning techniques for robust 3D segmentation and cell-cycle state identification of individual cell nuclei with widely varying morphologies embedded in a highly complex tumor environment. It enabled the study of the in vivo cell-cycle effects of cancer drugs on the largest data set to ever be reported in the realm of intravital microscopy.

Deepak’s doctoral research at the University of Houston was in the areas of medical image analysis and machine learning. His research involved the development of computational methods for the detection and localization of coronary artery calcium in non-contrast computed tomography (CT) data. In particular, he worked to develop (i) a set of knowledge-driven Markov random field (MRF) models for medical image segmentation, with an emphasis on the incorporation of prior information into the segmentation problem; (ii) graph-based methods for the segmentation of the inner-thoracic wall, diaphragm, and heart; and (iii) machine learning methods for the detection of coronary artery calcium and estimation of coronary artery zones.

Deepak’s Master of Science research at the University of Houston was on the topic of “Computer-Aided Breast Reconstructive Surgery.” In particular, he developed methods for (i) the quantitative analysis of breast morphology from 3D surface scans of the torso and (ii) a novel geometric model that captures the overall shape of the breast (with just a few parameters), which is coupled with a physics-based deformable model framework to fit the shape model to real data.

Deepak’s research has resulted in numerous publications, some of which have been featured in premier journals and conferences such as Nature Methods, Computer Vision and Pattern Recognition (CVPR), and Medical Image Computing and Computer Assisted Intervention (MICCAI). In recognition of his dissertation work, Deepak received the “Best Ph.D. Award” from the department of computer science at the University of Houston. He has served as a technical reviewer for several medical image analysis and computer vision journals and conferences, including IEEE Transactions on Image Processing (TIP), Computer Vision and Image Understanding (CVIU), Medical Image Analysis (MEDIA), IEEE Transactions of Biomedical Engineering (TBME), IEEE Journal of Biomedical and Health Informatics, PLOS ONE, and IEEE International Symposium on Biomedical Imaging (ISBI).

  1. B. Paniagua, H. Shah, P. Hernandez-Cerdan, F. Budin, D. Chittajallu, R. Walter, A. Mol, A. Khan, and J. Vimort, "Automatic quantification framework to detect cracks in teeth," in SPIE Medical Imaging, 2018. [URL]
  2. H. Greer, S. Gerber, M. Niethammer, R. Kwitt, M. McCormick, D. Chittajallu, N. Siekierski, M. Oetgen, K. Cleary, and S. Aylward, "Scoliosis screening and monitoring using self contained ultrasound and neural networks," in Proceedings of the IEEE International Symposium on Biomedical Imaging, 2018. [URL]
  3. D. Chittajallu, N. Siekierski, S. Lee, S. Gerber, J. Beezley, D. Manthey, D. Gutman, and L. Cooper, "Vectorized persistent homology representations for characterizing glandular architecture in histology images," in Proceedings of the IEEE International Symposium on Biomedical Imaging, 2018. [URL]
  4. D. Chittajallu, M. McCormick, S. Gerber, T. Czernuszewicz, R. Gessner, M. Willis, M. Niethammer, R. Kwitt, and S. Aylward, "Image-based methods for phase estimation, gating and temporal super-resolution of cardiac ultrasound," IEEE Transactions on Biomedical Engineering, pp. 1-1, 2018. [URL]
  5. D. Gutman, M. Khalilia, S. Lee, M. Nalisnik, Z. Mullen, J. Beezley, D. Chittajallu, D. Manthey, and L. Cooper, "The Digital Slide Archive: A Software Platform for Management, Integration, and Analysis of Histology for Cancer Research," Cancer Research, vol. 77, no. 21, pp. e75-e78, Nov. 2017. [URL]
  6. S. Gerber, M. Jallais, H. Greer, M. McCormick, S. Montgomery, B. Freeman, D. Kane, D. Chittajallu, N. Siekierski, and S. Aylward, "Automatic Estimation of the Optic Nerve Sheath Diameter from Ultrasound Images," in Imaging for Patient-Customized Simulations and Systems for Point-of-Care Ultrasound. Springer International Publishing, 2017, pp. 113-120. [URL]
  7. D. Chittajallu, S. Florian, R. Kohler, Y. Iwamoto, J. Orth, R. Weissleder, G. Danuser, and T. Mitchison, "In vivo cell-cycle profiling in xenograft tumors by quantitative intravital microscopy," Nature Methods, vol. 12, no. 6, pp. 577-585, Jun. 2015. [URL]
  8. D. Chittajallu, N. Paragios, and I. Kakadiaris, "An Explicit Shape-Constrained MRF-Based Contour Evolution Method for 2-D Medical Image Segmentation," IEEE Journal of Biomedical and Health Informatics, vol. 18, no. 1, pp. 120-129, Jan. 2014. [URL]
  9. U. Kurkure, D. Chittajallu, G. Brunner, Y. Le, and I. Kakadiaris, "A supervised classification-based method for coronary calcium detection in non-contrast CT," The International Journal of Cardiovascular Imaging, vol. 26, no. 7, pp. 817-828, Oct. 2010. [URL]
  10. G. Brunner, D. Chittajallu, U. Kurkure, and I. Kakadiaris, "Toward the automatic detection of coronary artery calcification in non-contrast computed tomography data," The International Journal of Cardiovascular Imaging, vol. 26, no. 7, pp. 829-838, Oct. 2010. [URL]
  11. D. Chen, D. Chittajallu, G. Passalis, and I. Kakadiaris, "Computational Tools for Quantitative Breast Morphometry Based on 3D Scans," Annals of Biomedical Engineering, vol. 38, no. 5, pp. 1703-1718, May 2010. [URL]
  12. D. Chittajallu, S. Shah, and I. Kakadiaris, "A shape-driven MRF model for the segmentation of organs in medical images," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2010. [URL]
  13. G. Brunner, D. Chittajallu, U. Kurkure, and I. Kakadiaris, "Patch-Cuts: A Graph-Based Image Segmentation Method Using Patch Features and Spatial Relations," in Proceedings of the British Machine Vision Conference, 2010. [URL]
  14. R. Yalamanchili, D. Chittajallu, P. Balanca, B. Tamarappoo, D. Berman, D. Dey, and I. Kakadiaris, "Automatic segmentation of the diaphragm in non-contrast CT images," in Proceedings of the IEEE International Symposium on Biomedical Imaging, 2010. [URL]
  15. D. Chittajallu, P. Balanca, and I. Kakadiaris, "Automatic delineation of the inner thoracic region in non-contrast CT data," in Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009. [URL]
  16. D. Chittajallu, G. Brunner, U. Kurkure, R. Yalamanchili, and I. Kakadiaris, "Fuzzy-Cuts: A knowledge-driven graph-based method for medical image segmentation," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2009. [URL]
  17. G. Brunner, D. Chittajallu, U. Kurkure, and I. Kakadiaris, "A Heart-Centered Coordinate System for the Detection of Coronary Artery Zones in Non-Contrast Computed Tomography Data," in Computer Vision in Intracardiac and Intravascular Imaging, 2008.
  18. U. Kurkure, D. Chittajallu, G. Brunner, R. Yalamanchili, and I. Kakadiaris, "Detection of coronary calcifications using supervised hierarchical classification," in Computer Vision in Intracardiac and Intravascular Imaging, 2008.
  19. G. Brunner, U. Kurkure, D. Chittajallu, R. Yalamanchili, and I. Kakadiaris, "Toward Unsupervised Classification of Calcified Arterial Lesions," in Medical Image Computing and Computer-Assisted Intervention, 2008. [URL]