About MIVIC is a research group in the Division of Physics, Engineering, Mathematics and Computer Science in Delaware State University. MIVIC's research interests include medical image analysis, mathematical methods for visual computing, machine learning, and visualization. More information about the group's research and training activities, publications, and group members can be found below: Research
Foundation models for healthcare: innovations in generative AI, computer vision, language models, and multimodal systems Submitted by admin on Thu, 01/15/2026 - 17:52 S. Makrogiannis, “Foundation models for healthcare: innovations in generative AI, computer vision, language models, and multimodal systems”, Frontiers in Computer Science, vol. 7, p. 1744581, 2025.
Measurements of acetabular morphology in healthy children using multiplanar computed tomography reconstructions Submitted by admin on Thu, 01/15/2026 - 17:51 L. Carlos Alm Da Silva, Kaymaz, B., Makrogiannis, S., Rogers, K. J., Kecskemethy, H. H., Nikam, R., Bowen, J. Richard, Gould, S. W., and Thacker, M. M., “Measurements of acetabular morphology in healthy children using multiplanar computed tomography reconstructions”, Journal of Pediatric Orthopaedics B, pp. 10–1097, 2025.
Scale selection and machine learning based cell segmentation and tracking in time lapse microscopy Submitted by admin on Thu, 07/24/2025 - 12:15 N. Annasamudram, Zhao, J., Oluwadare, O., Prashanth, A., and Makrogiannis, S., “Scale selection and machine learning based cell segmentation and tracking in time lapse microscopy”, Scientific Reports, vol. 15, p. 11717, 2025.
Artificial intelligence in pediatric osteopenia diagnosis: evaluating deep network classification and model interpretability using wrist X-rays Submitted by admin on Thu, 07/24/2025 - 12:13 C. E. Harris, Liu, L., Almeida, L., Kassick, C., and Makrogiannis, S., “Artificial intelligence in pediatric osteopenia diagnosis: evaluating deep network classification and model interpretability using wrist X-rays”, Bone Reports, vol. 25, p. 101845, 2025.
Pediatric osteopenia prediction and interpretation in wrist x-rays Submitted by admin on Thu, 07/24/2025 - 12:12 C. E. Harris, Liu, L., and Makrogiannis, S., “Pediatric osteopenia prediction and interpretation in wrist x-rays”, in Medical Imaging 2025: Computer-Aided Diagnosis, 2025.
Deep network and multi-atlas segmentation fusion for delineation of thigh muscle groups in three-dimensional water–fat separated MRI Submitted by admin on Fri, 09/13/2024 - 09:14 N. V. Annasamudram, Okorie, A. M., Spencer, R. G., Kalyani, R. R., Yang, Q., Landman, B. A., Ferrucci, L., and Makrogiannis, S., “Deep network and multi-atlas segmentation fusion for delineation of thigh muscle groups in three-dimensional water–fat separated MRI”, Journal of Medical Imaging, vol. 11, p. 054003, 2024.
Evaluating breast density classification by sparse approximation classifiers and deep networks using simulated digital mammograms Submitted by admin on Wed, 08/28/2024 - 12:51 C. E. Harris, Okorie, U., Bakic, P. R., and Makrogiannis, S., “Evaluating breast density classification by sparse approximation classifiers and deep networks using simulated digital mammograms”, in Virtual Imaging Trials in Medicine, 2024.
PET Imaging of Neurofibromatosis Type 1 with a Fluorine-18 Labeled Tryptophan Radiotracer Submitted by admin on Wed, 08/28/2024 - 12:51 X. Yue, Stauff, E., Boyapati, S., Langhans, S. A., Xu, W., Makrogiannis, S., Okorie, U. J., Okorie, A. M., Kandula, V. V. R., Kecskemethy, H. H., Nikam, R. M., Averill, L. W., and Shaffer, T. H., “PET Imaging of Neurofibromatosis Type 1 with a Fluorine-18 Labeled Tryptophan Radiotracer”, Pharmaceuticals, vol. 17, p. 685, 2024.
Multi-method and multi-atlas segmentation fusion for delineation of thigh muscle groups in 3D water-fat separated MRI Submitted by admin on Wed, 08/28/2024 - 12:50 N. V. Annasamudram, Okorie, A. M., Spencer, R. G., Kalyani, R. R., Yang, Q., Landman, B. A., Ferrucci, L., and Makrogiannis, S., “Multi-method and multi-atlas segmentation fusion for delineation of thigh muscle groups in 3D water-fat separated MRI”, in Medical Imaging 2024: Image Processing, 2024.
