International journal of computer assisted radiology and surgery · 2026
High-precision label-free virtual H&E staining of 3D holotomography using DAPI-guided conditional diffusion learning.
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Bak Taeyoung, Kim Sangwook, Ahn Daewoong, Min Hyun-Seok, Lee Jimin
Аннотация
PURPOSE: Conventional hematoxylin and eosin (H&E) staining is destructive and largely limited to two-dimensional sections. We aimed to develop a practical virtual staining method that produces H&E-like images from label-free three-dimensional holotomography (HT) while preserving nuclear morphology and requiring only HT at inference.
METHODS: We designed a DAPI-guided conditional diffusion model with a shared encoder and two decoder heads (H&E and DAPI). During training, the model receives HT as condition input and predicts diffusion noise for both H&E and DAPI targets using mean-squared-error objectives. DAPI is used only during training as nucleus-centric guidance. Data were acquired from the same tissue using a sequential protocol (HT imaging, then DAPI imaging, then H&E imaging). Because local nonlinear tissue deformation remains after global affine registration, HT-H&E pairs were treated as weakly paired, while HT-DAPI provided stronger local correspondence.
RESULTS: Compared with CycleGAN baselines, the proposed model produced more realistic nuclear morphology and better structural consistency. On held-out test tiles, DAPI-guided diffusion achieved lower FID and KID (FID: 9.2158; KID: 0.0091 ± 0.0035 ) than CycleGAN without DAPI (FID: 14.7447; KID: 0.0434 ± 0.0067 ).
CONCLUSIONS: Training-only DAPI guidance improves virtual H&E generation from label-free HT without requiring DAPI during inference. This weakly paired training design reduces dependence on expensive pixel-level registration and supports scalable, nondestructive digital histopathology workflows.
METHODS: We designed a DAPI-guided conditional diffusion model with a shared encoder and two decoder heads (H&E and DAPI). During training, the model receives HT as condition input and predicts diffusion noise for both H&E and DAPI targets using mean-squared-error objectives. DAPI is used only during training as nucleus-centric guidance. Data were acquired from the same tissue using a sequential protocol (HT imaging, then DAPI imaging, then H&E imaging). Because local nonlinear tissue deformation remains after global affine registration, HT-H&E pairs were treated as weakly paired, while HT-DAPI provided stronger local correspondence.
RESULTS: Compared with CycleGAN baselines, the proposed model produced more realistic nuclear morphology and better structural consistency. On held-out test tiles, DAPI-guided diffusion achieved lower FID and KID (FID: 9.2158; KID: 0.0091 ± 0.0035 ) than CycleGAN without DAPI (FID: 14.7447; KID: 0.0434 ± 0.0067 ).
CONCLUSIONS: Training-only DAPI guidance improves virtual H&E generation from label-free HT without requiring DAPI during inference. This weakly paired training design reduces dependence on expensive pixel-level registration and supports scalable, nondestructive digital histopathology workflows.