# Multimodal Alignment of Histopathological Images Using Cell Segmentation and Point Set Matching for Integrative Cancer Analysis

arXiv: https://arxiv.org/abs/2410.00152 · HTML: https://mlsanity.com/paper/2410.00152/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 30 Sep 2024.

## Builds on (most cited first, 6 of 6)

- [Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy](https://mlsanity.com/paper/1908.03636/) (2019)
- [Multilevel Graph Matching Networks for Deep Graph Similarity Learning](https://mlsanity.com/paper/2007.04395/) (2020)
- [Comparison of Consecutive and Re-stained Sections for Image Registration in Histopathology](https://mlsanity.com/paper/2106.13150/) (2021)
- [Ink removal from histopathology whole slide images by combining classification, detection and image generation models](https://mlsanity.com/paper/1905.04385/) (2019)
- [Digital pathology-based study of cell- and tissue-level morphologic features in serous borderline ovarian tumor and high-grade serous ovarian cancer](https://mlsanity.com/paper/2008.12479/) (2020)
- [Fast Coherent Point Drift](https://mlsanity.com/paper/2006.06281/) (2020)

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

- [CORE -- A Cell-Level Coarse-to-Fine Image Registration Engine for Multi-stain Image Alignment](https://mlsanity.com/paper/2511.03826/) (2025)

## Abstract (arXiv)

Histopathological imaging is vital for cancer research and clinical practice, with multiplexed Immunofluorescence (MxIF) and Hematoxylin and Eosin (H&E) providing complementary insights. However, aligning different stains at the cell level remains a challenge due to modality differences. In this paper, we present a novel framework for multimodal image alignment using cell segmentation outcomes. By treating cells as point sets, we apply Coherent Point Drift (CPD) for initial alignment and refine it with Graph Matching (GM). Evaluated on ovarian cancer tissue microarrays (TMAs), our method achieves high alignment accuracy, enabling integration of cell-level features across modalities and generating virtual H&E images from MxIF data for enhanced clinical interpretation.