bibtext: @InProceedings{ Aze_Deep_MICCAI2024,
                   author = { Azevedo, Caio and Santra, Sanchayan and Kumawat, Sudhakar and Nagahara, Hajime and Morooka, Ken'ichi },
                   title = { { Deep Volume Reconstruction from Multi-focus Microscopic Images } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15004 },
                   month = {October},
                   pages = { pending },
                }
abstract: Reconstructing 3D volumes from optical microscopic images
  is useful in important areas such as cellular analysis, cancer research, and drug development. However, existing techniques either require specialized hardware or extensive sample preprocessing. Recently, Yamaguchi et al proposed to solve this problem by just using a single stack of optical microscopic images with different focus settings and reconstructing
  a voxel-based representation of the observation using the classical iterative optimization method. Inspired by this result, this work aims to explore this method further using new state-of-the-art optimization techniques such as Deep Image Prior (DIP). Our analysis showcases the superiority of this approach over Yamaguchi et al in reconstruction quality, hard metrics, and robustness to noise on the synthetic data. Finally, we also demonstrate the effectiveness of our approach on real data,
  producing excellent reconstruction quality.  Code available at: https://github.com/caiocj1/multifocus-3d-reconstruction.
title: Deep Volume Reconstruction from Multi-focus Microscopic Images
authors: Azevedo, Caio Santra, Sanchayan Kumawat, Sudhakar Nagahara, Hajime Morooka, Ken'ichi