Current Landscape of Advanced Imaging Tools for Pathology Diagnostics

Abraham, Tanishq Mathew; Levenson, Richard

doi:10.1016/j.modpat.2024.100443

Cited by 3 publications

(2 citation statements)

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“…The automated selection of targets for high-resolution analysis in fixed samples enormously reduces storage efforts and makes the observation of rare phenotypes with increased statistical sampling possible [40]. For example, when coupled to artificial intelligence, the approach can have a big impact in the contribution of microscopy to pathology [41][42][43][44]. In basic biomedical research, the presented methodologies are now gaining an increasing space with event-driven acquisitions [40,[45][46][47].…”

Section: Discussionmentioning

confidence: 99%

From Cell Populations to Molecular Complexes: Multiplexed Multimodal Microscopy to Explore p53-53BP1 Molecular Interaction

Pelicci,

Furia,

Pelicci

et al. 2024

IJMS

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Surpassing the diffraction barrier revolutionized modern fluorescence microscopy. However, intrinsic limitations in statistical sampling, the number of simultaneously analyzable channels, hardware requirements, and sample preparation procedures still represent an obstacle to its widespread diffusion in applicative biomedical research. Here, we present a novel pipeline based on automated multimodal microscopy and super-resolution techniques employing easily available materials and instruments and completed with open-source image-analysis software developed in our laboratory. The results show the potential impact of single-molecule localization microscopy (SMLM) on the study of biomolecules’ interactions and the localization of macromolecular complexes. As a demonstrative application, we explored the basis of p53-53BP1 interactions, showing the formation of a putative macromolecular complex between the two proteins and the basal transcription machinery in situ, thus providing visual proof of the direct role of 53BP1 in sustaining p53 transactivation function. Moreover, high-content SMLM provided evidence of the presence of a 53BP1 complex on the cell cytoskeleton and in the mitochondrial space, thus suggesting the existence of novel alternative 53BP1 functions to support p53 activity.

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Section: Discussionmentioning

confidence: 99%

From Cell Populations to Molecular Complexes: Multiplexed Multimodal Microscopy to Explore p53-53BP1 Molecular Interaction

Pelicci,

Furia,

Pelicci

et al. 2024

IJMS

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“…Training these neural networks should involve the same tissue section after staining, adjacent sections at various distances (e.g., 50, 100, 200, 500, and 1,000 μm), and sections from different tissues to perform multi-step cross-validation of the ML tools. Both supervised and unsupervised training strategies can be employed for this task ( Abraham and Levenson, 2024 ; Alajaji et al, 2024 ; Pillar et al, 2024 ).…”

Section: Discussionmentioning

confidence: 99%

ML-driven segmentation of microvascular features during histological examination of tissue-engineered vascular grafts

Danilov,

Laptev,

Klyshnikov

et al. 2024

Front. Bioeng. Biotechnol.

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IntroductionThe development of next-generation tissue-engineered medical devices such as tissue-engineered vascular grafts (TEVGs) is a leading trend in translational medicine. Microscopic examination is an indispensable part of animal experimentation, and histopathological analysis of regenerated tissue is crucial for assessing the outcomes of implanted medical devices. However, the objective quantification of regenerated tissues can be challenging due to their unusual and complex architecture. To address these challenges, research and development of advanced ML-driven tools for performing adequate histological analysis appears to be an extremely promising direction.MethodsWe compiled a dataset of 104 representative whole slide images (WSIs) of TEVGs which were collected after a 6-month implantation into the sheep carotid artery. The histological examination aimed to analyze the patterns of vascular tissue regeneration in TEVGs in situ. Having performed an automated slicing of these WSIs by the Entropy Masker algorithm, we filtered and then manually annotated 1,401 patches to identify 9 histological features: arteriole lumen, arteriole media, arteriole adventitia, venule lumen, venule wall, capillary lumen, capillary wall, immune cells, and nerve trunks. To segment and quantify these features, we rigorously tuned and evaluated the performance of six deep learning models (U-Net, LinkNet, FPN, PSPNet, DeepLabV3, and MA-Net).ResultsAfter rigorous hyperparameter optimization, all six deep learning models achieved mean Dice Similarity Coefficients (DSC) exceeding 0.823. Notably, FPN and PSPNet exhibited the fastest convergence rates. MA-Net stood out with the highest mean DSC of 0.875, demonstrating superior performance in arteriole segmentation. DeepLabV3 performed well in segmenting venous and capillary structures, while FPN exhibited proficiency in identifying immune cells and nerve trunks. An ensemble of these three models attained an average DSC of 0.889, surpassing their individual performances.ConclusionThis study showcases the potential of ML-driven segmentation in the analysis of histological images of tissue-engineered vascular grafts. Through the creation of a unique dataset and the optimization of deep neural network hyperparameters, we developed and validated an ensemble model, establishing an effective tool for detecting key histological features essential for understanding vascular tissue regeneration. These advances herald a significant improvement in ML-assisted workflows for tissue engineering research and development.

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