Pathology-Guided Virtual Staining Metric for Evaluation and Training
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909692430647296 |
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| author | Wang, Qiankai Tweel, James E. D. Reza, Parsin Haji Layton, Anita |
| author_facet | Wang, Qiankai Tweel, James E. D. Reza, Parsin Haji Layton, Anita |
| contents | Virtual staining has emerged as a powerful alternative to traditional histopathological staining techniques, enabling rapid, reagent-free image transformations. However, existing evaluation methods predominantly rely on full-reference image quality assessment (FR-IQA) metrics such as structural similarity, which are originally designed for natural images and often fail to capture pathology-relevant features. Expert pathology reviews have also been used, but they are inherently subjective and time-consuming.
In this study, we introduce PaPIS (Pathology-Aware Perceptual Image Similarity), a novel FR-IQA metric specifically tailored for virtual staining evaluation. PaPIS leverages deep learning-based features trained on cell morphology segmentation and incorporates Retinex-inspired feature decomposition to better reflect histological perceptual quality. Comparative experiments demonstrate that PaPIS more accurately aligns with pathology-relevant visual cues and distinguishes subtle cellular structures that traditional and existing perceptual metrics tend to overlook. Furthermore, integrating PaPIS as a guiding loss function in a virtual staining model leads to improved histological fidelity.
This work highlights the critical need for pathology-aware evaluation frameworks to advance the development and clinical readiness of virtual staining technologies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_12624 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Pathology-Guided Virtual Staining Metric for Evaluation and Training Wang, Qiankai Tweel, James E. D. Reza, Parsin Haji Layton, Anita Image and Video Processing Computer Vision and Pattern Recognition Systems and Control Virtual staining has emerged as a powerful alternative to traditional histopathological staining techniques, enabling rapid, reagent-free image transformations. However, existing evaluation methods predominantly rely on full-reference image quality assessment (FR-IQA) metrics such as structural similarity, which are originally designed for natural images and often fail to capture pathology-relevant features. Expert pathology reviews have also been used, but they are inherently subjective and time-consuming. In this study, we introduce PaPIS (Pathology-Aware Perceptual Image Similarity), a novel FR-IQA metric specifically tailored for virtual staining evaluation. PaPIS leverages deep learning-based features trained on cell morphology segmentation and incorporates Retinex-inspired feature decomposition to better reflect histological perceptual quality. Comparative experiments demonstrate that PaPIS more accurately aligns with pathology-relevant visual cues and distinguishes subtle cellular structures that traditional and existing perceptual metrics tend to overlook. Furthermore, integrating PaPIS as a guiding loss function in a virtual staining model leads to improved histological fidelity. This work highlights the critical need for pathology-aware evaluation frameworks to advance the development and clinical readiness of virtual staining technologies. |
| title | Pathology-Guided Virtual Staining Metric for Evaluation and Training |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Systems and Control |
| url | https://arxiv.org/abs/2507.12624 |