Treatment classification of posterior capsular opacification (PCO) using automated ground truths

TL;DR

This paper presents a deep learning approach for segmenting and classifying posterior capsular opacification images to determine treatment necessity, utilizing both manual and automated ground truths for training.

Contribution

The study introduces a DL-based method that effectively uses automated ground truths for training, achieving high segmentation accuracy and clinical classification performance.

Findings

Dice coefficient > 0.8 for segmentation

IoU score > 0.67 for segmentation

F2-score of 0.98 for classification

Abstract

Determination of treatment need of posterior capsular opacification (PCO)-- one of the most common complication of cataract surgery -- is a difficult process due to its local unavailability and the fact that treatment is provided only after PCO occurs in the central visual axis. In this paper we propose a deep learning (DL)-based method to first segment PCO images then classify the images into \textit{treatment required} and \textit{not yet required} cases in order to reduce frequent hospital visits. To train the model, we prepare a training image set with ground truths (GT) obtained from two strategies: (i) manual and (ii) automated. So, we have two models: (i) Model 1 (trained with image set containing manual GT) (ii) Model 2 (trained with image set containing automated GT). Both models when evaluated on validation image set gave Dice coefficient value greater than 0.8 and intersection-over-union (IoU) score greater than 0.67 in our experiments. Comparison between gold standard GT and segmented results from our models gave a Dice coefficient value greater than 0.7 and IoU score greater than 0.6 for both the models showing that automated ground truths can also result in generation of an efficient model. Comparison between our classification result and clinical classification shows 0.98 F2-score for outputs from both the models.