Theodoros Pissas
Papers
5
Total Citations
29
H-Index
3
About
Theodoros Pissas is a leading researcher in the intersection of ophthalmic imaging, computer vision, and robotic surgery. His primary research areas include intra-operative optical coherence tomography (iOCT) image enhancement, super-resolution, and deep learning-based tracking for retinal procedures. Pissas’s major contribution is the development of a supervised deep convolutional neural network that jointly predicts semantic segmentation and optical flow for intra-operative retinal tracking—a critical enabler for robotic delivery of regenerative therapies. His work has garnered significant attention, with his most cited paper, “Learned optical flow for intra-operative tracking of the retinal fundus” (2020), accumulating 12 citations. He has further advanced the field through a series of publications on iOCT super-resolution, including a two-stage methodology leveraging high-quality pre-operative scans and an unpaired video super-resolution approach using contrastive learning. Pissas’s research directly addresses the challenge of real-time visualization of retinal layers during surgery, which is essential for precise subretinal injection of sight-restoring therapies. His innovative use of deep learning to enhance iOCT image quality and enable robotic-assisted procedures positions him at the forefront of surgical vision systems for ophthalmology.
Research Focus
Key Achievements
Top Papers
- 1Learned optical flow for intra-operative tracking of the retinal fundus12 citations · 2020
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