Improving OCT Image Segmentation of Retinal Layers by Utilizing a Machine Learning Based Multistage System of Stacked Multiscale Encoders and Decoders.

Optical coherence tomography (OCT)-based retinal imagery is often utilized to determine influential factors in patient progression and treatment, for which the retinal layers of the human eye are investigated to assess a patient's health status and eyesight. In this contribution, we propose a machine learning (ML)-based multistage system of stacked multiscale encoders and decoders for the image segmentation of OCT imagery of the retinal layers to enable the following evaluation regarding the physiological and pathological states. Our proposed system's results highlight its benefits compared to currently investigated approaches by combining commonly deployed methods from deep learning (DL) while utilizing deep neural networks (DNN). We conclude that by stacking multiple multiscale encoders and decoders, improved scores for the image segmentation task can be achieved. Our retinal-layer-based segmentation results in a final segmentation performance of up to 82.25±0.74% for the Sørensen-Dice coefficient, outperforming the current best single-stage model by 1.55% with a score of 80.70±0.20%, given the evaluated peripapillary OCT data set. Additionally, we provide results on the data sets Duke SD-OCT, Heidelberg, and UMN to illustrate our model's performance on especially noisy data sets.

[Extraction of features from clinical routine data using text mining]. / Merkmalsextraktion aus klinischen Routinedaten mittels Text-Mining.

BACKGROUND: Anti-VEGF drugs are currently used to treat macular diseases. This has led to a wealth of additional data, which could help understand and predict treatment courses; however, this information is usually only available in free text form. OBJECTIVE: A retrospective study was designed to analyze how far interpretable information can be obtained from clinical texts by automated extraction. The aim was to assess the suitability of a text mining method that was customized for this purpose. MATERIAL AND METHODS: Data on 3683 patients were available, including 40,485 discharge letters. Some of the data of interest, e.g. visual acuity (VA), intraocular pressure (IOP) and accompanying diagnoses, were not only recorded textually but also entered in a database and could thus serve as a gold standard for text analysis. The text was analyzed using the Averbis Health Discovery text mining platform. To optimize the extraction task, rule knowledge and a German language technical vocabulary linked to the international medical terminology standard systematized nomenclature of medicine (SNOMED CT) was manually added. RESULTS: The correspondence between extracted data and the structured database entries is described by the F1 value. There was agreement of 94.7% for VA, 98.3% for IOP and 94.7% for the accompanying diagnoses. Manual analysis of noncorresponding cases showed that in 50% text content did not match the database content for various reasons. After an adjustment, F1 values 1-3% above the previously determined values were obtained. CONCLUSION: Text mining procedures are very well suited for the considered discharge letter corpus and the problem described in order to extract contents from clinical texts in a structured manner for further evaluation.