AIMC Topic: Paraffin Embedding

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Exploratory multi-cohort, multi-reader study on the clinical utility of a deep learning model for transforming cryosectioned to formalin-fixed, paraffin-embedded (FFPE) images in breast lesion diagnosis.

Breast cancer research : BCR Jun 17, 2025
BACKGROUND: Cryosectioned tissues often exhibit artifacts that compromise pathologists' diagnostic accuracy during intraoperative assessments. These inconsistencies, compounded by variations in frozen section (FS) production across laboratories, high...
Breast Image Interpretation, Computer-Assisted Formaldehyde Paraffin Embedding Cryoultramicrotomy Deep Learning Breast Neoplasms Frozen Sections Image Processing, Computer-Assisted Humans Cohort Studies Tissue Fixation Female
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A Multi-Perspective Self-Supervised Generative Adversarial Network for FS to FFPE Stain Transfer.

IEEE transactions on medical imaging Feb 4, 2025
In clinical practice, frozen section (FS) images can be utilized to obtain the immediate pathological results of the patients in operation due to their fast production speed. However, compared with the formalin-fixed and paraffin-embedded (FFPE) imag...
Humans Image Processing, Computer-Assisted Frozen Sections Paraffin Embedding Supervised Machine Learning Staining and Labeling Generative Adversarial Networks
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A deep-learning model for transforming the style of tissue images from cryosectioned to formalin-fixed and paraffin-embedded.

Nature biomedical engineering Dec 23, 2022
Histological artefacts in cryosectioned tissue can hinder rapid diagnostic assessments during surgery. Formalin-fixed and paraffin-embedded (FFPE) tissue provides higher quality slides, but the process for obtaining them is laborious (typically lasti...
Paraffin Embedding Lung Neoplasms Deep Learning Formaldehyde Carcinoma, Non-Small-Cell Lung Humans
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Artificial intelligence-augmented histopathologic review using image analysis to optimize DNA yield from formalin-fixed paraffin-embedded slides.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc Oct 5, 2022
To achieve minimum DNA input requirements for next-generation sequencing (NGS), pathologists visually estimate macrodissection and slide count decisions. Unfortunately, misestimation may cause tissue waste and increased laboratory costs. We developed...
Paraffin Embedding Formaldehyde Artificial Intelligence Humans Neoplasms DNA
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Three-Dimensional Vessel Segmentation in Whole-Tissue and Whole-Block Imaging Using a Deep Neural Network: Proof-of-Concept Study.

The American journal of pathology Dec 18, 2020
In the field of pathology, micro-computed tomography (micro-CT) has become an attractive imaging modality because it enables full analysis of the three-dimensional characteristics of a tissue sample or organ in a noninvasive manner. However, because ...
Neural Networks, Computer Imaging, Three-Dimensional Software Humans X-Ray Microtomography Paraffin Embedding Proof of Concept Study
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Diagnosis of T-cell-mediated kidney rejection in formalin-fixed, paraffin-embedded tissues using RNA-Seq-based machine learning algorithms.

Human pathology Oct 6, 2018
Molecular diagnosis is being increasingly used in transplant pathology to render more objective and quantitative determinations that also provide mechanistic and prognostic insights. This study performed RNA-Seq on biopsies from kidneys with stable f...
Tissue Fixation Paraffin Embedding Adult Formaldehyde Sequence Analysis, RNA Humans Kidney Transplantation Middle Aged T-Lymphocytes Machine Learning Graft Rejection Algorithms Young Adult Aged Female Male
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