AGMDT: Virtual Staining of Renal Histology Images with Adjacency-Guided Multi-Domain Transfer

• URL: http://arxiv.org/abs/2309.06421v2
• Date: Sun, 17 Sep 2023 10:35:30 GMT
• Title: AGMDT: Virtual Staining of Renal Histology Images with Adjacency-Guided Multi-Domain Transfer
• Authors: Tao Ma, Chao Zhang, Min Lu, Lin Luo
• Abstract summary: We propose a novel virtual staining framework AGMDT to translate images into other domains by avoiding pixel-level alignment. Based on it, the proposed framework AGMDT discovers patch-level aligned pairs across the serial slices of multi-domains through glomerulus detection and bipartite graph matching. Experimental results show that the proposed AGMDT achieves a good balance between the precise pixel-level alignment and unpaired domain transfer.
• Score: 9.8359439975283
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Renal pathology, as the gold standard of kidney disease diagnosis, requires doctors to analyze a series of tissue slices stained by H&E staining and special staining like Masson, PASM, and PAS, respectively. These special staining methods are costly, time-consuming, and hard to standardize for wide use especially in primary hospitals. Advances of supervised learning methods have enabled the virtually conversion of H&E images into special staining images, but achieving pixel-to-pixel alignment for training remains challenging. In contrast, unsupervised learning methods regarding different stains as different style transfer domains can utilize unpaired data, but they ignore the spatial inter-domain correlations and thus decrease the trustworthiness of structural details for diagnosis. In this paper, we propose a novel virtual staining framework AGMDT to translate images into other domains by avoiding pixel-level alignment and meanwhile utilizing the correlations among adjacent tissue slices. We first build a high-quality multi-domain renal histological dataset where each specimen case comprises a series of slices stained in various ways. Based on it, the proposed framework AGMDT discovers patch-level aligned pairs across the serial slices of multi-domains through glomerulus detection and bipartite graph matching, and utilizes such correlations to supervise the end-to-end model for multi-domain staining transformation. Experimental results show that the proposed AGMDT achieves a good balance between the precise pixel-level alignment and unpaired domain transfer by exploiting correlations across multi-domain serial pathological slices, and outperforms the state-of-the-art methods in both quantitative measure and morphological details.

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