# Point Projection Mapping System for Tracking, Registering, Labeling and Validating Optical Tissue Measurements

arXiv: https://arxiv.org/abs/2311.13378 · HTML: https://mlsanity.com/paper/2311.13378/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 40 references that are in the map.
- First posted on arXiv 22 Nov 2023.

## Builds on (most cited first, 2 of 2)

- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [An Unsupervised Learning Model for Deformable Medical Image Registration](https://mlsanity.com/paper/1802.02604/) (2018)

## Built on by (most cited first, 1 of 1)

- [Deformable multi-modal image registration for the correlation between optical measurements and histology images](https://mlsanity.com/paper/2311.14414/) (2023)

## Abstract (arXiv)

Validation of newly developed optical tissue sensing techniques for tumor detection during cancer surgery requires an accurate correlation with histological results. Additionally, such accurate correlation facilitates precise data labeling for developing high-performance machine-learning tissue classification models. In this paper, a newly developed Point Projection Mapping system will be introduced, which allows non-destructive tracking of the measurement locations on tissue specimens. Additionally, a framework for accurate registration, validation, and labeling with histopathology results is proposed and validated on a case study. The proposed framework provides a more robust and accurate method for tracking and validation of optical tissue sensing techniques, which saves time and resources compared to conventional techniques available.