Confluence of Deep Learning Using Watershed Segmentation GAN for Advancing Endoscopy Surgery Imaging

Abstract

Accurate segmentation in medical images is critical for effective diagnosis and treatment. This study presents a novel approach using a watershed-segmented Generative Adversarial Network (GAN) for segmentation in the Cholec80 laparoscopic cholecystectomy videos. Initially, a watershed algorithm preprocesses the images, providing robust initial segmentation that highlights potential lesion boundaries. This segmented output trains a GAN, which refines and improves segmentation accuracy. The GAN comprises a generator producing segmentation masks and a discriminator evaluating their realism against ground truth. Evaluated on the Cholec80 dataset, our approach demonstrates significant improvements in segmentation accuracy over existing methods. Quantitative results indicate superior performance in dice coefficient, intersection over union (IoU), and other metrics. Qualitative analysis supports the efficacy of our method in accurately delineating boundaries in complex surgical scenes. This integration presents a promising direction for enhancing medical image analysis.