Holotomography in 2025: From Morphometric Imaging to AI-Driven Multimodal Phenotyping
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866909982207770624 |
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| author | Park, YongKeun |
| author_facet | Park, YongKeun |
| contents | By 2025, holotomography (HT) has matured from a niche optical modality into a versatile platform for quantitative, label-free imaging in biomedicine. By reconstructing the three-dimensional refractive-index (RI) distribution of cells and tissues, HT enables high-resolution volumetric imaging with low phototoxicity and minimal sample perturbation. This Review surveys recent advances in the field and highlights three emerging directions: (i) the incorporation of deep-learning approaches for virtual staining, phenotypic classification, and automated analysis; (ii) the extension of HT to structurally complex biological systems, including organoids and thick tissue specimens; and (iii) the integration of HT with complementary modalities, such as Raman and polarization-sensitive microscopy, to enhance molecular and biophysical specificity. We summarize current HT applications spanning subcellular phenotyping, metabolic and mechanical profiling, and early-stage clinical studies in areas such as infectious disease and pathology. Finally, we discuss remaining technical and translational challenges and outline a roadmap for the prospective integration of HT into digital pathology and high-throughput screening workflows. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_02611 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Holotomography in 2025: From Morphometric Imaging to AI-Driven Multimodal Phenotyping Park, YongKeun Biological Physics By 2025, holotomography (HT) has matured from a niche optical modality into a versatile platform for quantitative, label-free imaging in biomedicine. By reconstructing the three-dimensional refractive-index (RI) distribution of cells and tissues, HT enables high-resolution volumetric imaging with low phototoxicity and minimal sample perturbation. This Review surveys recent advances in the field and highlights three emerging directions: (i) the incorporation of deep-learning approaches for virtual staining, phenotypic classification, and automated analysis; (ii) the extension of HT to structurally complex biological systems, including organoids and thick tissue specimens; and (iii) the integration of HT with complementary modalities, such as Raman and polarization-sensitive microscopy, to enhance molecular and biophysical specificity. We summarize current HT applications spanning subcellular phenotyping, metabolic and mechanical profiling, and early-stage clinical studies in areas such as infectious disease and pathology. Finally, we discuss remaining technical and translational challenges and outline a roadmap for the prospective integration of HT into digital pathology and high-throughput screening workflows. |
| title | Holotomography in 2025: From Morphometric Imaging to AI-Driven Multimodal Phenotyping |
| topic | Biological Physics |
| url | https://arxiv.org/abs/2601.02611 |