label-free-high-resolution-3d-imaging-machine-learning-analysis

Label-free, High-Resolution 3D Imaging and Machine Learning Analysis of Intestinal Organoids via Low-Coherence Holotomography

Accurate, label-free imaging of intestinal organoids is crucial for studying their morphology, growth dynamics, and responses to environmental stimuli. ...

KAIST

This study analyzed the effects of a school-based mind subtraction meditation program on depression, social anxiety, aggression, and salivary cortisol levels of 42 elementary school children in South Korea. The research design was a nonequivalent group comparison with pretest and post-test. The experimental group was given 8weeks of the meditation program. The results showed social anxiety, aggression, and salivary cortisol levels were significantly lowered in the experimental group. This demonstrated that the school-based mind subtraction meditation program could be effective in improving psychosocial and behavioral aspects of mental health in elementary school children.

The Effects of Mind Subtraction Meditation on Depression, Social Anxiety, Aggression, and Salivary Cortisol Levels of Elementary School Children in South Korea.

Clear cell renal cell carcinoma (ccRCC) is a common histopathological subtype of renal cancer and is notorious for its poor prognosis. Its accurate diagnosis by histopathology, which relies on manual microscopic inspection of stained slides, is challenging. Here, we present a correlative approach to utilize stained images and refractive index (RI) tomography and demonstrate quantitative assessments of the structural heterogeneities of ccRCC slides obtained from human patients. Machine-learning-assisted segmentation of nuclei and cytoplasm enabled the quantification at the subcellular level. Compared to benign regions, malignant regions exhibited a considerable increase in structural heterogeneities. The results demonstrate that RI tomography provides quantitative information in synergy with stained images on the structural heterogeneities in ccRCC.

Quantification of structural heterogeneity in H&E stained clear cell renal cell carcinoma using refractive index tomography.

We developed a machine learning algorithm (MLA) that can classify human thyroid cell clusters by exploiting both Papanicolaou staining and intrinsic refractive index (RI) as correlative imaging contrasts and evaluated the effects of this combination on diagnostic performance. Thyroid fine-needle aspiration biopsy (FNAB) specimens were analyzed using correlative optical diffraction tomography, which can simultaneously measure both, the color brightfield of Papanicolaou staining and three-dimensional RI distribution. The MLA was designed to classify benign and malignant cell clusters using color images, RI images, or both. We included 1535 thyroid cell clusters (benign: malignancy = 1128:407) from 124 patients. Accuracies of MLA classifiers using color images, RI images, and both were 98.0%, 98.0%, and 100%, respectively. As information for classification, the nucleus size was mainly used in the color image; however, detailed morphological information of the nucleus was also used in the RI image. We demonstrate that the present MLA and correlative FNAB imaging approach has the potential for diagnosing thyroid cancer, and complementary information from color and RI images can improve the performance of the MLA.

Machine-learning-based diagnosis of thyroid fine-needle aspiration biopsy synergistically by Papanicolaou staining and refractive index distribution.

Quantitative phase imaging, integrated with artificial intelligence, allows for the rapid and label-free investigation of the physiology and pathology of biological systems. This review presents the principles of various two-dimensional and three-dimensional label-free phase imaging techniques that exploit refractive index as an intrinsic optical imaging contrast. In particular, we discuss artificial intelligence-based analysis methodologies for biomedical studies including image enhancement, segmentation of cellular or subcellular structures, classification of types of biological samples and image translation to furnish subcellular and histochemical information from label-free phase images. We also discuss the advantages and challenges of artificial intelligence-enabled quantitative phase imaging analyses, summarize recent notable applications in the life sciences, and cover the potential of this field for basic and industrial research in the life sciences.

Artificial intelligence-enabled quantitative phase imaging methods for life sciences.

In histopathology, acquiring subcellular-level three-dimensional (3D) tissue structures efficiently and without damaging the tissues during serial sectioning and staining remains a formidable challenge. We address this by integrating holotomography with deep learning and creating 3D virtual hematoxylin and eosin (H&E) images from label-free thick cancer tissues. This method involves measuring the tissues' 3D refractive index (RI) distribution using holotomography, followed by processing with a deep learning-based image translation framework to produce virtual H&E staining in 3D. Applied to colon cancer tissues up to 50 µm thick-far surpassing conventional slide thickness-this technique provides direct methodological validation through chemical H&E staining. It reveals quantitative 3D microanatomical structures of colon cancer with subcellular resolution. Further validation of our method's repeatability and scalability is demonstrated on gastric cancer samples across different institutional settings. This innovative 3D virtual H&E staining method enhances histopathological efficiency and reliability, marking a significant advancement in extending histopathology to the 3D realm and offering substantial potential for cancer research and diagnostics.

Article	Year
The Effects of Mind Subtraction Meditation on Depression, Social Anxiety, Aggression, and Salivary Cortisol Levels of Elementary School Children in South Korea. Journal of pediatric nursing\| PubMed ID: 26790919	2016
Quantification of structural heterogeneity in H&E stained clear cell renal cell carcinoma using refractive index tomography. Biomedical optics express\| PubMed ID: 36950245	2023
Machine-learning-based diagnosis of thyroid fine-needle aspiration biopsy synergistically by Papanicolaou staining and refractive index distribution. Scientific reports\| PubMed ID: 37330568	2023
Artificial intelligence-enabled quantitative phase imaging methods for life sciences. Nature methods\| PubMed ID: 37872244	2023
Revealing 3D microanatomical structures of unlabeled thick cancer tissues using holotomography and virtual H&E staining. Nature communications\| PubMed ID: 40404616	2025

Juyeon Park

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