# Topology-based deep-learning segmentation method for deep anterior lamellar keratoplasty (DALK) surgical guidance using M-mode OCT data

arXiv: https://arxiv.org/abs/2501.04735 · HTML: https://mlsanity.com/paper/2501.04735/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 9 references that are in the map.
- First posted on arXiv 7 Jan 2025.

## Builds on (most cited first, 3 of 3)

- [Star Shape Prior in Fully Convolutional Networks for Skin Lesion Segmentation](https://mlsanity.com/paper/1806.08437/) (2018)
- [The Neural Networks Based Needle Detection for Medical Retinal Surgery](https://mlsanity.com/paper/2302.05034/) (2023)
- [Reimagining partial thickness keratoplasty: An eye mountable robot for autonomous big bubble needle insertion](https://mlsanity.com/paper/2410.14577/) (2024)

## Built on by (most cited first, 5 of 5)

- [Super-Resolution Optical Coherence Tomography Using Diffusion Model-Based Plug-and-Play Priors](https://mlsanity.com/paper/2505.14916/) (2025)
- [End-to-end reconstruction of OCT optical properties and speckle-reduced structural intensity via physics-based learning](https://mlsanity.com/paper/2602.02721/) (2026)
- [Physics-based generation of multilayer corneal OCT data via Gaussian modeling and MCML for AI-driven diagnostic and surgical guidance applications](https://mlsanity.com/paper/2602.02755/) (2026)
- [Super-Resolution and Denoising of Corneal B-Scan OCT Imaging Using Diffusion Model Plug-and-Play Priors](https://mlsanity.com/paper/2602.02795/) (2026)
- [Real-time topology-aware M-mode OCT segmentation for robotic deep anterior lamellar keratoplasty (DALK) guidance](https://mlsanity.com/paper/2602.02798/) (2026)

## Abstract (arXiv)

Deep Anterior Lamellar Keratoplasty (DALK) is a partial-thickness corneal transplant procedure used to treat corneal stromal diseases. A crucial step in this procedure is the precise separation of the deep stroma from Descemet's membrane (DM) using the Big Bubble technique. To simplify the tasks of needle insertion and pneumo-dissection in this technique, we previously developed an Optical Coherence Tomography (OCT)-guided, eye-mountable robot that uses real-time tracking of corneal layers from M-mode OCT signals for control. However, signal noise and instability during manipulation of the OCT fiber sensor-integrated needle have hindered the performance of conventional deep-learning segmentation methods, resulting in rough and inaccurate detection of corneal layers. To address these challenges, we have developed a topology-based deep-learning segmentation method that integrates a topological loss function with a modified network architecture. This approach effectively reduces the effects of noise and improves segmentation speed, precision, and stability. Validation using in vivo, ex vivo, and hybrid rabbit eye datasets demonstrates that our method outperforms traditional loss-based techniques, providing fast, accurate, and robust segmentation of the epithelium and DM to guide surgery.