Bayesian Deep Learning Approaches for Uncertainty-Aware Retinal OCT Image Segmentation for Multiple Sclerosis

• URL: http://arxiv.org/abs/2505.12061v2
• Date: Tue, 20 May 2025 12:37:32 GMT
• Title: Bayesian Deep Learning Approaches for Uncertainty-Aware Retinal OCT Image Segmentation for Multiple Sclerosis
• Authors: Samuel T. M. Ball,
• Abstract summary: Optical Coherence Tomography ( OCT) provides valuable insights in ophthalmology, cardiology, and neurology.<n>One critical task for ophthalmologists using OCT is delineation of retinal layers within scans.<n>Previous efforts to automate delineation using deep learning face challenges in uptake from clinicians and statisticians.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Optical Coherence Tomography (OCT) provides valuable insights in ophthalmology, cardiology, and neurology due to high-resolution, cross-sectional images of the retina. One critical task for ophthalmologists using OCT is delineation of retinal layers within scans. This process is time-consuming and prone to human bias, affecting the accuracy and reliability of diagnoses. Previous efforts to automate delineation using deep learning face challenges in uptake from clinicians and statisticians due to the absence of uncertainty estimation, leading to "confidently wrong" models via hallucinations. In this study, we address these challenges by applying Bayesian convolutional neural networks (BCNNs) to segment an openly available OCT imaging dataset containing 35 human retina OCTs split between healthy controls and patients with multiple sclerosis. Our findings demonstrate that Bayesian models can be used to provide uncertainty maps of the segmentation, which can further be used to identify highly uncertain samples that exhibit recording artefacts such as noise or miscalibration at inference time. Our method also allows for uncertainty-estimation for important secondary measurements such as layer thicknesses, that are medically relevant for patients. We show that these features come in addition to greater performance compared to similar work over all delineations; with an overall Dice score of 95.65%. Our work brings greater clinical applicability, statistical robustness, and performance to retinal OCT segmentation.

Related papers

• Lightweight Relational Embedding in Task-Interpolated Few-Shot Networks for Enhanced Gastrointestinal Disease Classification [0.0]
  Colon cancer detection is crucial for increasing patient survival rates.<n> colonoscopy is dependent on obtaining adequate and high-quality endoscopic images.<n>Few-Shot Learning architecture enables our model to rapidly adapt to unseen fine-grained endoscopic image patterns.<n>Our model demonstrated superior performance, achieving an accuracy of 90.1%, precision of 0.845, recall of 0.942, and an F1 score of 0.891.
  arXiv  Detail & Related papers  (2025-05-30T16:54:51Z)
• Multimodal Deep Learning for Stroke Prediction and Detection using Retinal Imaging and Clinical Data [0.7322887425853788]
  This study explores the impact of leveraging retinal images and clinical data for stroke detection and risk prediction.<n>We propose a multimodal deep neural network that processes Optical Coherence Tomography ( OCT) and infrared reflectance retinal scans.<n>Our empirical findings establish the predictive ability of the considered modalities in detecting lasting effects in the retina associated with acute stroke.
  arXiv  Detail & Related papers  (2025-05-05T14:22:58Z)
• 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)
• nnUNet RASPP for Retinal OCT Fluid Detection, Segmentation and Generalisation over Variations of Data Sources [25.095695898777656]
  We propose two variants of the nnUNet with consistent high performance across images from multiple device vendors. The algorithm was validated on the MICCAI 2017 RETOUCH challenge dataset. Experimental results show that our algorithms outperform the current state-of-the-arts algorithms.
  arXiv  Detail & Related papers  (2023-02-25T23:47:23Z)
• Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images [55.83984261827332]
  In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network. We develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module. Our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches.
  arXiv  Detail & Related papers  (2022-12-01T07:32:56Z)
• Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis [88.39466012709205]
  This paper builds a novel medical slice to increase the between-slice resolution. Considering that the ground-truth intermediate medical slices are always absent in clinical practice, we introduce the incremental cross-view mutual distillation strategy. Our method outperforms state-of-the-art algorithms by clear margins.
  arXiv  Detail & Related papers  (2021-12-20T03:38:37Z)
• Assessing glaucoma in retinal fundus photographs using Deep Feature Consistent Variational Autoencoders [63.391402501241195]
  glaucoma is challenging to detect since it remains asymptomatic until the symptoms are severe. Early identification of glaucoma is generally made based on functional, structural, and clinical assessments. Deep learning methods have partially solved this dilemma by bypassing the marker identification stage and analyzing high-level information directly to classify the data.
  arXiv  Detail & Related papers  (2021-10-04T16:06:49Z)
• Malignancy Prediction and Lesion Identification from Clinical Dermatological Images [65.1629311281062]
  We consider machine-learning-based malignancy prediction and lesion identification from clinical dermatological images. We first identify all lesions present in the image regardless of sub-type or likelihood of malignancy, then it estimates their likelihood of malignancy, and through aggregation, it also generates an image-level likelihood of malignancy.
  arXiv  Detail & Related papers  (2021-04-02T20:52:05Z)
• An Interpretable Multiple-Instance Approach for the Detection of referable Diabetic Retinopathy from Fundus Images [72.94446225783697]
  We propose a machine learning system for the detection of referable Diabetic Retinopathy in fundus images. By extracting local information from image patches and combining it efficiently through an attention mechanism, our system is able to achieve high classification accuracy. We evaluate our approach on publicly available retinal image datasets, in which it exhibits near state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-03-02T13:14:15Z)
• 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)
• Modeling and Enhancing Low-quality Retinal Fundus Images [167.02325845822276]
  Low-quality fundus images increase uncertainty in clinical observation and lead to the risk of misdiagnosis. We propose a clinically oriented fundus enhancement network (cofe-Net) to suppress global degradation factors. Experiments on both synthetic and real images demonstrate that our algorithm effectively corrects low-quality fundus images without losing retinal details.
  arXiv  Detail & Related papers  (2020-05-12T08:01:16Z)
• 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.