SpecstatOR: Speckle statistics-based iOCT Segmentation Network for Ophthalmic Surgery

• URL: http://arxiv.org/abs/2404.19481v1
• Date: Tue, 30 Apr 2024 11:49:29 GMT
• Title: SpecstatOR: Speckle statistics-based iOCT Segmentation Network for Ophthalmic Surgery
• Authors: Kristina Mach, Hessam Roodaki, Michael Sommersperger, Nassir Navab,
• Abstract summary: We use statistical analysis of speckle patterns to incorporate statistical pathology-specific prior knowledge. Our findings indicate statistically different speckle patterns within the retina and between retinal layers and surgical tools. The proposed segmentation model aims to refine the statistical findings based on prior tissue understanding.
• Score: 39.66047935237083
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents an innovative approach to intraoperative Optical Coherence Tomography (iOCT) image segmentation in ophthalmic surgery, leveraging statistical analysis of speckle patterns to incorporate statistical pathology-specific prior knowledge. Our findings indicate statistically different speckle patterns within the retina and between retinal layers and surgical tools, facilitating the segmentation of previously unseen data without the necessity for manual labeling. The research involves fitting various statistical distributions to iOCT data, enabling the differentiation of different ocular structures and surgical tools. The proposed segmentation model aims to refine the statistical findings based on prior tissue understanding to leverage statistical and biological knowledge. Incorporating statistical parameters, physical analysis of light-tissue interaction, and deep learning informed by biological structures enhance segmentation accuracy, offering potential benefits to real-time applications in ophthalmic surgical procedures. The study demonstrates the adaptability and precision of using Gamma distribution parameters and the derived binary maps as sole inputs for segmentation, notably enhancing the model's inference performance on unseen data.

Related papers

• A Multi-Dataset Classification-Based Deep Learning Framework for Electronic Health Records and Predictive Analysis in Healthcare [0.5999777817331317]
  This study proposes a novel deep learning predictive analysis framework for classifying multiple datasets. A hybrid deep learning model combining Residual Networks and Artificial Neural Networks is proposed to detect acute and chronic diseases. Rigorous experimentation and evaluation resulted in high accuracies of 93%, 99%, and 95% for retinal fundus images, cirrhosis stages, and heart disease diagnostic predictions, respectively.
  arXiv  Detail & Related papers  (2024-09-25T08:13:39Z)
• Strategies to Improve Real-World Applicability of Laparoscopic Anatomy Segmentation Models [6.8726432208129555]
  We systematically analyze the impact of class characteristics, training and test data composition, and modeling parameters on eight segmentation metrics. Our findings support two adjustments to account for data biases in surgical data science.
  arXiv  Detail & Related papers  (2024-03-25T21:08:26Z)
• Ambiguous Medical Image Segmentation using Diffusion Models [60.378180265885945]
  We introduce a single diffusion model-based approach that produces multiple plausible outputs by learning a distribution over group insights. Our proposed model generates a distribution of segmentation masks by leveraging the inherent sampling process of diffusion. Comprehensive results show that our proposed approach outperforms existing state-of-the-art ambiguous segmentation networks.
  arXiv  Detail & Related papers  (2023-04-10T17:58:22Z)
• A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applications [56.458448869572294]
  We introduce DETRtime, a novel framework for time-series segmentation of EEG data. Our end-to-end deep learning-based framework brings advances in Computer Vision to the forefront. Our model generalizes well in the task of EEG sleep stage segmentation.
  arXiv  Detail & Related papers  (2022-06-17T10:17:24Z)
• OCTAVA: an open-source toolbox for quantitative analysis of optical coherence tomography angiography images [0.2621533844622817]
  We report a user-friendly, open-source toolbox, OCTAVA, to automate the pre-processing, segmentation, and quantitative analysis of en face OCTA maximum intensity projection images. We perform quantitative analysis of OCTA images from different commercial and non-commercial instruments and samples and show OCTAVA can accurately and reproducibly determine metrics for characterization of microvasculature.
  arXiv  Detail & Related papers  (2021-09-04T10:11:42Z)
• Interpretable and synergistic deep learning for visual explanation and statistical estimations of segmentation of disease features from medical images [0.0]
  Deep learning (DL) models for disease classification or segmentation from medical images are increasingly trained using transfer learning (TL) from unrelated natural world images. We report detailed comparisons, rigorous statistical analysis and comparisons of widely used DL architecture for binary segmentation after TL. A free GitHub repository of TII and LMI models, code and more than 10,000 medical images and their Grad-CAM output from this study can be used as starting points for advanced computational medicine.
  arXiv  Detail & Related papers  (2020-11-11T14:08:17Z)
• Assignment Flow for Order-Constrained OCT Segmentation [0.0]
  The identification of retinal layer thicknesses serves as an essential task be done for each patient separately. The elaboration of automated segmentation models has become an important task in the field of medical image processing. We propose a novel, purely data driven textitgeometric approach to order-constrained 3D OCT retinal cell layer segmentation
  arXiv  Detail & Related papers  (2020-09-10T01:57:53Z)
• Benchmarking off-the-shelf statistical shape modeling tools in clinical applications [53.47202621511081]
  We systematically assess the outcome of widely used, state-of-the-art SSM tools. We propose validation frameworks for anatomical landmark/measurement inference and lesion screening. ShapeWorks and Deformetrica shape models are found to capture clinically relevant population-level variability.
  arXiv  Detail & Related papers  (2020-09-07T03:51:35Z)
• Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model [71.80319052891817]
  We present a relation-driven semi-supervised framework for medical image classification. It exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations. Our method outperforms many state-of-the-art semi-supervised learning methods on both single-label and multi-label image classification scenarios.
  arXiv  Detail & Related papers  (2020-05-15T06:57:54Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)

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.