Quantifying Vascular Density in Tissue Engineered Constructs Using Machine Learning

Strobel, Hannah A.; Schultz, Alex; Moss, Sarah M.; Eli, Rob; Hoying, James B.

doi:10.3389/fphys.2021.650714

Cited by 14 publications

(5 citation statements)

References 20 publications

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“…The applicability of ML-based models to a wide range of animal species is fundamental to their integration into pathophysiological studies and the development of tissue-engineering medical devices. Previously, ML algorithms have demonstrated their efficiency in high-throughput measurements of vascular density in phase contrast and fluorescent images of 3D tissue-engineered constructs derived from human and rat adipose tissue ( Strobel et al, 2021 ). In this study, ML-based software was able to annotate blood vessels in a 3D scaffold, further calculating blood vessel length and density within a given area to evaluate angiogenesis.…”

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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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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“…For the second method, a machine learning analysis was used (BioSegment, Advanced Solutions, Louisville, KY) [33]. The software was trained to recognize vessels by manually tracing more than 50 phase-contrast 100X images prior to the analysis.…”

Section: Plos Onementioning

confidence: 99%

Diabetes diminishes muscle precursor cell-mediated microvascular angiogenesis

2023

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The skeletal muscles of Type II diabetic (T2D) patients can be characterized by a reduced vessel density, corresponding to deficiencies in microvascular angiogenesis. Interestingly, T2D also inhibits the function of many myogenic cells resident within skeletal muscle, including satellite cells, which are well-known for the role they play in maintaining homeostasis. The current study was undertaken to gain a better understanding of the mechanisms whereby satellite cell progeny, muscle precursor cells (MPCs), influence microvascular angiogenesis. Network growth and the expression of genes associated with angiogenesis were reduced when microvessels were treated with conditioned media generated by proliferating MPCs isolated from diabetic, as compared to control rat skeletal muscle, a phenomenon that was also observed when myoblasts from control or diabetic human skeletal muscle were used. When only exosomes derived from diabetic or control MPCs were used to treat microvessels, no differences in microvascular growth were observed. An evaluation of the angiogenesis factors in control and diabetic MPCs revealed differences in Leptin, vascular endothelial growth factor (VEGF), IL1-β, interleukin 10, and IP-10, and an evaluation of the MPC secretome revealed differences in interleukin 6, MCP-1, VEGF, and interleukin 4 exist. Angiogenesis was also reduced in tissue-engineered skeletal muscles (TE-SkM) containing microvessels when they were generated from MPCs isolated from diabetic as compared to control skeletal muscle. Lastly, the secretome of injured control, but not diabetic, TE-SkM was able to increase VEGF and increase microvascular angiogenesis. This comprehensive analysis of the interaction between MPCs and microvessels in the context of diabetes points to an area for alleviating the deleterious effects of diabetes on skeletal muscle.

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“…A semi-automated ML-based system has been introduced to quantify vascular density in tissueengineered constructs (Strobel et al, 2021). This tool's fast and accurate measuring capacity makes it perfect for incorporation into tissue manufacturing workflows.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Techniques for Quantiﬁcation of Tumour Necrosis in Post-neoadjuvant Chemotherapy Osteosarcoma Resection Specimens for Eﬀective Treatment Planning

Saleena¹,

Ilyas²,

Sajna³

et al. 2023

AIP

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Osteosarcoma is a high-grade malignant bone tumour for which neoadjuvant chemotherapy is a vital component of the treatment plan. Chemotherapy brings about the death of tumour tissues, and the rate of their death is an essential factor in deciding on further treatment. The necrosis quantification is now done manually by visualizing tissue sections through the microscope. This is a crude method that can cause significant inter-observer bias. The suggested system is an AI-based therapeutic decision-making tool that can automatically calculate the quantity of such dead tissue present in a tissue specimen. We employ U-Net++ and DeepLabv3+, pre-trained deep learning algorithms for the segmentation purpose. ResNet50 and ResNet101 are used as encoder parts of U-Net++ and DeepLabv3+, respectively. Also, we synthesize a dataset of 555 patches from 37 images captured and manually annotated by experienced pathologists. Dice loss and Intersection over Union (IoU) are used as the performance metrics. The training and testing IoU of U-Net++ are 91.78% and 82.64%, and its loss is 4.4% and 17.77%, respectively. The IoU and loss of DeepLabv3+ are 91.09%, 81.50%, 4.77%, and 17.8%, respectively. The results show that both models perform almost similarly. With the help of this tool, necrosis segmentation can be done more accurately while requiring less work and time. The percentage of segmented regions can be used as the decision-making factor in the further treatment plans.

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Quantifying Vascular Density in Tissue Engineered Constructs Using Machine Learning

Cited by 14 publications

References 20 publications

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

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

Diabetes diminishes muscle precursor cell-mediated microvascular angiogenesis

Deep Learning Techniques for Quantiﬁcation of Tumour Necrosis in Post-neoadjuvant Chemotherapy Osteosarcoma Resection Specimens for Eﬀective Treatment Planning

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