Topology-based deep-learning segmentation method for deep anterior lamellar keratoplasty (DALK) surgical guidance using M-mode OCT data
- URL: http://arxiv.org/abs/2501.04735v1
- Date: Tue, 07 Jan 2025 19:57:15 GMT
- Title: Topology-based deep-learning segmentation method for deep anterior lamellar keratoplasty (DALK) surgical guidance using M-mode OCT data
- Authors: J. Yu, H. Yi, Y. Wang, J. D. Opfermann, W. G. Gensheimer, A. Krieger, J. U. Kang,
- Abstract summary: We develop 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.
- Score: 0.0
- License:
- Abstract: 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.
Related papers
- SPRMamba: Surgical Phase Recognition for Endoscopic Submucosal Dissection with Mamba [4.37495931705689]
We propose SPRMamba, a novel Mamba-based framework for ESD surgical phase recognition.
We show that SPRMamba surpasses existing state-of-the-art methods and exhibits greater robustness across various surgical phase recognition tasks.
arXiv Detail & Related papers (2024-09-18T16:26:56Z) - Neurovascular Segmentation in sOCT with Deep Learning and Synthetic Training Data [4.5276169699857505]
This study demonstrates a synthesis engine for neurovascular segmentation in serial-section optical coherence tomography images.
Our approach comprises two phases: label synthesis and label-to-image transformation.
We demonstrate the efficacy of the former by comparing it to several more realistic sets of training labels, and the latter by an ablation study of synthetic noise and artifact models.
arXiv Detail & Related papers (2024-07-01T16:09:07Z) - CT-based brain ventricle segmentation via diffusion Schrödinger Bridge without target domain ground truths [0.9720086191214947]
Efficient and accurate brain ventricle segmentation from clinical CT scans is critical for emergency surgeries like ventriculostomy.
We introduce a novel uncertainty-aware ventricle segmentation technique without the need of CT segmentation ground truths.
Our method employs the diffusion Schr"odinger Bridge and an attention recurrent residual U-Net to capitalize on unpaired CT and MRI scans.
arXiv Detail & Related papers (2024-05-28T15:17:58Z) - CathFlow: Self-Supervised Segmentation of Catheters in Interventional Ultrasound Using Optical Flow and Transformers [66.15847237150909]
We introduce a self-supervised deep learning architecture to segment catheters in longitudinal ultrasound images.
The network architecture builds upon AiAReSeg, a segmentation transformer built with the Attention in Attention mechanism.
We validated our model on a test dataset, consisting of unseen synthetic data and images collected from silicon aorta phantoms.
arXiv Detail & Related papers (2024-03-21T15:13:36Z) - EyeLS: Shadow-Guided Instrument Landing System for Intraocular Target
Approaching in Robotic Eye Surgery [51.05595735405451]
Robotic ophthalmic surgery is an emerging technology to facilitate high-precision interventions such as retina penetration in subretinal injection and removal of floating tissues in retinal detachment.
Current image-based methods cannot effectively estimate the needle tip's trajectory towards both retinal and floating targets.
We propose to use the shadow positions of the target and the instrument tip to estimate their relative depth position.
Our method succeeds target approaching on a retina model, and achieves an average depth error of 0.0127 mm and 0.3473 mm for floating and retinal targets respectively in the surgical simulator.
arXiv Detail & Related papers (2023-11-15T09:11:37Z) - AiAReSeg: Catheter Detection and Segmentation in Interventional
Ultrasound using Transformers [75.20925220246689]
endovascular surgeries are performed using the golden standard of Fluoroscopy, which uses ionising radiation to visualise catheters and vasculature.
This work proposes a solution using an adaptation of a state-of-the-art machine learning transformer architecture to detect and segment catheters in axial interventional Ultrasound image sequences.
arXiv Detail & Related papers (2023-09-25T19:34:12Z) - Solving Low-Dose CT Reconstruction via GAN with Local Coherence [2.325977856241404]
We propose a novel approach using generative adversarial networks (GANs) with enhanced local coherence.
The proposed method can capture the local coherence of adjacent images by optical flow, which yields significant improvements in the precision and stability of the constructed images.
arXiv Detail & Related papers (2023-09-24T08:55:42Z) - Reconstructing the somatotopic organization of the corticospinal tract
remains a challenge for modern tractography methods [55.07297021627281]
The corticospinal tract (CST) is a critically important white matter fiber tract in the human brain that enables control of voluntary movements of the body.
Diffusion MRI tractography is the only method that enables the study of the anatomy and variability of the CST pathway in human health.
arXiv Detail & Related papers (2023-06-09T02:05:40Z) - Deep learning network to correct axial and coronal eye motion in 3D OCT
retinal imaging [65.47834983591957]
We propose deep learning based neural networks to correct axial and coronal motion artifacts in OCT based on a single scan.
The experimental result shows that the proposed method can effectively correct motion artifacts and achieve smaller error than other methods.
arXiv Detail & Related papers (2023-05-27T03:55:19Z) - Orientation-Shared Convolution Representation for CT Metal Artifact
Learning [63.67718355820655]
During X-ray computed tomography (CT) scanning, metallic implants carrying with patients often lead to adverse artifacts.
Existing deep-learning-based methods have gained promising reconstruction performance.
We propose an orientation-shared convolution representation strategy to adapt the physical prior structures of artifacts.
arXiv Detail & Related papers (2022-12-26T13:56:12Z) - Segmentation of Retinal Low-Cost Optical Coherence Tomography Images
using Deep Learning [2.571523045125397]
The need for treatment is determined by the presence or change of disease-specific OCT-based biomarkers.
The monitoring frequency of current treatment schemes is not individually adapted to the patient and therefore often insufficient.
One of the key requirements of a home monitoring OCT system is a computer-aided diagnosis to automatically detect and quantify pathological changes.
arXiv Detail & Related papers (2020-01-23T12:55:53Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.