Extraction of Text from Optic Nerve Optical Coherence Tomography Reports

TL;DR

This study developed and evaluated rule-based OCR algorithms to accurately extract retinal nerve fiber layer and ganglion cell data from Zeiss Cirrus OCT reports, enabling efficient large-scale data processing.

Contribution

The paper introduces a customized OCR-based method for high-precision extraction of OCT report data, improving automation in ophthalmic data analysis.

Findings

High precision in data extraction for RNFL and GCC reports

Slightly better extraction accuracy for right eye RNFL and left eye GCC

Challenges remain in extracting specific values like clock hours and signal strength

Abstract

Purpose: The purpose of this study was to develop and evaluate rule-based algorithms to enhance the extraction of text data, including retinal nerve fiber layer (RNFL) values and other ganglion cell count (GCC) data, from Zeiss Cirrus optical coherence tomography (OCT) scan reports. Methods: DICOM files that contained encapsulated PDF reports with RNFL or Ganglion Cell in their document titles were identified from a clinical imaging repository at a single academic ophthalmic center. PDF reports were then converted into image files and processed using the PaddleOCR Python package for optical character recognition. Rule-based algorithms were designed and iteratively optimized for improved performance in extracting RNFL and GCC data. Evaluation of the algorithms was conducted through manual review of a set of RNFL and GCC reports. Results: The developed algorithms demonstrated high precision in extracting data from both RNFL and GCC scans. Precision was slightly better for the right eye in RNFL extraction (OD: 0.9803 vs. OS: 0.9046), and for the left eye in GCC extraction (OD: 0.9567 vs. OS: 0.9677). Some values presented more challenges in extraction, particularly clock hours 5 and 6 for RNFL thickness, and signal strength for GCC. Conclusions: A customized optical character recognition algorithm can identify numeric results from optical coherence scan reports with high precision. Automated processing of PDF reports can greatly reduce the time to extract OCT results on a large scale.