Abstract
The generation of smoke in laparoscopic surgery due to laser ablation and cauterization causes deterioration in the visual quality of the operative field. In order to reduce the effect of smoke, the present paper proposes an end-to-end network, called Cycle-Desmoke. The network enhances the CycleGAN framework by adoption of a new generator architecture and addition of new Guided-Unsharp Upsample loss in combination to adversarial and cycle-consistency loss. The Atrous Convolution Feature Extraction Module present in the encoder blocks of the generator helps distinguishing smoke by capturing features at multiple scales by the use of kernels with different receptive fields. Further, the use of Guided-Unsharp Upsample loss supervises the upsampling process of the feature maps and helps improve the contrast of the desmoked image. The network performs robust unsupervised Image-to-Image Translation from smoke domain to smoke-free domain. The public Cholec80 dataset is used to evaluate the performance of the proposed method. Quantitative and qualitative comparative analysis of the proposed method over the state-of-the-methods reveals the effectiveness of the method at the task of smoke removal and enhancement of the image.
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Vishal, V., Sharma, N., Singh, M. (2019). Guided Unsupervised Desmoking of Laparoscopic Images Using Cycle-Desmoke. In: Zhou, L., et al. OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging. OR 2.0 MLCN 2019 2019. Lecture Notes in Computer Science(), vol 11796. Springer, Cham. https://doi.org/10.1007/978-3-030-32695-1_3
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