# Real-time Non-Rigid 3D Reconstruction and Registration for Laparoscopic-guided Minimally Invasive Liver Surgery

> **NIH NIH K99** · BRIGHAM AND WOMEN'S HOSPITAL · 2020 · $93,240

## Abstract

Project Summary/Abstract
Liver deformation leads to difficulties in tumor localization during minimally invasive liver surgery (MILS). The
goal of this proposal is to develop an efficient surgical navigation tool for MILS by compensating for liver
deformation and mapping preoperative data to the patient’s anatomy. Specifically, we will develop a non-rigid
simultaneously localization and mapping (SLAM) approach to estimate the deformation of liver surface from
stereo laparoscopy videos. We will develop machine-learning methods to detect landmarks and perform non-
rigid registration. The algorithms will be implemented on a GPU to achieve real-time. Preliminary data has
demonstrated the feasibility. During the R00 phase, we will mainly address the clinical needs and develop novel
ways to provide intraoperative guidance. This project will greatly improve the tumor resection accuracy in MILS.
The candidate for this award Dr. Haoyin Zhou is a postdoc at Surgical Planning Laboratory (SPL), Brigham and
Women’s Hospital (BWH) and Harvard Medical School (HMS). Dr. Zhou has extensive experience and expertise
in computer vision, machine learning and their applications in medicine. BWH is an international leader in basic,
clinical and translational research on human diseases, and has established multiple research programs to
promote the work and professional career development of young investigators. National Center for Image Guided
Therapy, and Advanced Multi-modality Image Guided Operating (AMIGO) suite will greatly support this research.
Dr. Zhou’s long-term research goal is to develop and apply advanced computer vision and machine learning
technologies to improve understanding, diagnosis, treatment, and prevention of diseases for better health care.
His long-term career goal is to become an independent investigator working at the frontier of medical image
processing and image-guided therapy. To achieve these goals, Dr. Zhou plans to receive more education and
training in the following four areas: (1) Critical training in conducting translational research in the hospital
environment with surgeons and radiologists, (2) knowledge in the development of technologies for surgical
guidance, (3) training in machine learning and its applications in medicine, and (4) training on writing grant
applications independently and seeking funding. Dr. Zhou will participate in formal courses selected from Harvard,
Harvard Catalyst, MIT CSAIL and Stanford Courses. He will attend weekly seminars at BWH, HMS and MIT. He
will also attend one or two academic conferences per year to discuss his work and meet with experts in the field.
A strong mentoring team, including one primary mentor, three co-mentors, and two collaborators, has been
organized for the K99 phase of this award, which will provide solid support on both research and career
development to Dr. Zhou based on their well-established expertise in diverse research fields. Prof. William M.
Wells III (primary mentor) is a...

## Key facts

- **NIH application ID:** 10017968
- **Project number:** 5K99EB027177-02
- **Recipient organization:** BRIGHAM AND WOMEN'S HOSPITAL
- **Principal Investigator:** Haoyin Zhou
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $93,240
- **Award type:** 5
- **Project period:** 2019-09-15 → 2021-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10017968

## Citation

> US National Institutes of Health, RePORTER application 10017968, Real-time Non-Rigid 3D Reconstruction and Registration for Laparoscopic-guided Minimally Invasive Liver Surgery (5K99EB027177-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10017968. Licensed CC0.

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