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Determination of treatment need of posterior capsular opacification (PCO)--one of the most common complication of cataract surgery -- is a difficultprocess due to its local unavailability and the fact that treatment is providedonly after PCO occurs in the central visual axis. In this paper we propose adeep learning (DL)-based method to first segment PCO images then classify theimages into \textit{treatment required} and \textit{not yet required} cases inorder to reduce frequent hospital visits. To train the model, we prepare atraining image set with ground truths (GT) obtained from two strategies: (i)manual and (ii) automated. So, we have two models: (i) Model 1 (trained withimage set containing manual GT) (ii) Model 2 (trained with image set containingautomated GT). Both models when evaluated on validation image set gave Dicecoefficient value greater than 0.8 and intersection-over-union (IoU) scoregreater than 0.67 in our experiments. Comparison between gold standard GT andsegmented results from our models gave a Dice coefficient value greater than0.7 and IoU score greater than 0.6 for both the models showing that automatedground truths can also result in generation of an efficient model. Comparisonbetween our classification result and clinical classification shows 0.98F2-score for outputs from both the models.

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