Taito Manabe

About

Taito Manabe is a researcher at the forefront of medical robotics and real-time computer vision, with a focus on practical, hardware-efficient solutions. His work primarily spans two critical domains: autonomous surgical systems and embedded vision for automotive safety. In surgical technology, Manabe addresses a key bottleneck in laparoscopic procedures—the reliance on human camera assistants. His 2019 work on CNN architectures for surgical image segmentation introduced a recursive network structure specifically designed to mitigate overfitting, a crucial step toward enabling reliable, robot-controlled laparoscopes that could allow surgeons to operate independently. This contribution has garnered 5 citations and represents a meaningful step toward more autonomous operating rooms. On the hardware side, Manabe’s 2022 research on FPGA implementation of contour detection, based on the Helmholtz principle, tackles the challenge of low-cost, real-time white line detection for advanced driver assistance systems. By achieving efficient contour detection on field-programmable gate arrays, his work demonstrates how sophisticated computer vision algorithms can be deployed without expensive, power-hungry GPUs, making self-driving technologies more accessible. Together, these contributions highlight Manabe’s talent for bridging algorithmic innovation with practical, real-world deployment constraints.

Research Focus

Key Achievements

Top Papers

1. 1
   CNN Architecture for Surgical Image Segmentation Systems with Recursive Network Structure to Mitigate Overfitting

   5 citations · 2019
2. 2
   FPGA implementation of contour detection based on Helmholtz principle

   4 citations · 2022

Key Collaborators

Contact & Links