Chronological Registration of OCT and Autofluorescence Findings in CSCR: Two Distinct Patterns in Disease Course

Abstract

Optical coherence tomography (OCT) and fundus autofluorescence (FAF) are important imaging modalities for the assessment and prognosis of central serous chorioretinopathy (CSCR). However, setting the findings from both into spatial and temporal contexts as desirable for disease analysis remains a challenge due to both modalities being captured in different perspectives: sparse three-dimensional (3D) cross sections for OCT and two-dimensional (2D) en face images for FAF. To bridge this gap, we propose a visualisation pipeline capable of projecting OCT labels to en face image modalities such as FAF. By mapping OCT B-scans onto the accompanying en face infrared (IR) image and then registering the IR image onto the FAF image by a neural network, we can directly compare OCT labels to other labels in the en face plane. We also present a U-Net inspired segmentation model to predict segmentations in unlabeled OCTs. Evaluations show that both our networks achieve high precision (0.853 Dice score and 0.913 Area under Curve). Furthermore, medical analysis performed on exemplary, chronologically arranged CSCR progressions of 12 patients visualized with our pipeline indicates that, on CSCR, two patterns emerge: subretinal fluid (SRF) in OCT preceding hyperfluorescence (HF) in FAF and vice versa.

When Two Eyes Don’t Suffice—Learning Difficult Hyperfluorescence Segmentations in Retinal Fundus Autofluorescence Images via Ensemble Learning

“…As in our previous works [33,34], the base of our data stems from 326 patients with CSCR, collected from 2003 to 2020. From this, we sampled patients for our retrospective study.…”

“…For our work, as in [33,34], we sample from the database of the University Eye Clinic of Kiel, Germany, a tertiary care center that is specialized on CSCR patients. This unique large database consists of over 300 long-term CSCR disease courses with a median follow-up of 2.5 years.…”

Integrating an AI Platform into Clinical IT: BPMN Processes for Clinical AI Model Development

“…Each of the A&I projects is working on applying state-ofthe-art machine learning (ML) methods on a speci c clinical use case. These use cases employ a broad variety of areas where ML can be utilized from analysis of patients' alphanumerical data produced by medical devices at the ICU [23] to medical image analysis [24]. The output of the AI platform project includes the requirements analysis for identifying and documenting the needs as well as objectives for developing a clinical AI platform.…”

Deep Learning in Medical Image Registration: Introduction and Survey

“…Uni 1-to-1 Coarse-f. Classical Bones (Saiti et al, 2022) Multimodal CT Uni 1-to-1 1 Supervised (Santarossa et al, 2022) Multimodal IR-FAF/OCT Uni 1-to-1 1 Classical Eye (Schmidt et al, 2022) Uni 1-to-1 Coarse-f. Unsupervised Veins (Su et al, 2021) Unimodal CT/MR Uni 1-to-1 Classical (Terpstra et al, Unimodal greyscale Uni 1-to-1 Coarse-f. Classical Brain (Yang et al, 2022) Multimodal MR Uni 1-to-1 1 Unsupervised Prostate (Ye et al, 2021) Unimodal MR Bi 1-to-1 1 Unsupervised Heart (Ying et al, 2022) Unimodal MR Uni 1-to-1 1 Classical Breast (Zhang, G. et al, 2021) Unimodal Uni 1-to-1 Pyramid Unsupervised Brain Unimodal MR Uni 1-to-1 Pyramid Unsupervised Brain (Zhu et al, 2021) Unimodal MR Uni 1-to-1 Pyramid Unsupervised Head…”