ArXiv Preprint
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.
Raisha Shrestha, Waree Kongprawechnon, Teesid Leelasawassuk, Nattapon Wongcumchang, Oliver Findl, Nino Hirnschall
2022-11-11
