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- Description:
- This dataset accompanies the research article entitled, "Etiology-Specific Remodeling in Ventricular Tissue of Heart Failure Patients and its Implications for Computational Modeling of Electrical Conduction," where we quantified fibrosis and performed electrophysiological simulation to investigate electrical propagation in etiologically varied heart failure tissue samples. Included are raw confocal microscopic images, data for extracting and processing the raw images and script to analyze fibrosis and generate meshes for simulation.
- Keyword:
- human, heart failure, cardiac, confocal microscopic images, simulation, and fibrosis
- Subject:
- cardiology
- Creator:
- Drakos, Stavros G., Sachse, Frank B., Kyriakopoulos, Christos P., Bragard, Jean, Greiner, Joachim, Chakkalakkal Sankarankutty, Aparna, Visker, Joseph R., and Shankar, Thirupura S.
- Owner:
- Aparna Sankarankutty
- Based Near Label Tesim:
- Salt Lake City, Utah, United States
- Language:
- English
- Date Uploaded:
- 06/08/2021
- Date Modified:
- 12/08/2023
- Date Created:
- 2019-01-01 to 2020-11-30
- License:
- CC BY NC - Allows others to use and share your data non-commercially and with attribution.
- Resource Type:
- Dataset
- Identifier:
- https://doi.org/10.7278/S50D-BPS8-R06S
- Description:
- Significance: Current medical imaging systems have many limitations for applications in cardiovascular diseases. New technologies may overcome these limitations. Particularly interesting are technologies for diagnosis of cardiac diseases, e.g. fibrosis, myocarditis, and transplant rejection. Aim: To introduce and assess a new optical system capable of assessing cardiac muscle tissue using light-scattering spectroscopy (LSS) in conjunction with machine learning. Approach: We applied an ovine model to investigate if the new LSS system is capable of estimating densities of cell nuclei in cardiac tissue. We measured the nuclear density using fluorescent labeling, confocal microscopy, and image processing. Spectra acquired from the same cardiac tissues were analyzed with spectral clustering and convolutional neural networks to assess feasibility and reliability of density quantification. Results: Spectral clustering revealed distinct groups of spectra correlated to ranges of nuclear density. Convolutional neural networks correctly classified 3 groups of spectra with low, medium, or high nuclear density with 95.00±11.77% (mean and standard deviation) accuracy. The analysis revealed sensitivity of the accuracy to wavelength range and subsampling of spectra. Conclusions: LSS and machine learning are capable of assessing nuclear density in cardiac tissues. The approach could be useful for diagnosis of cardiac diseases associated with an increase of nuclei.
- Keyword:
- machine learning, diagnosis, neural networks, medical imaging, nuclear density, spectroscopy, spectral imaging, and cardiac
- Subject:
- Cardiology
- Creator:
- Sachse, Frank B., Tiwari, Sarthak, Kaza, Aditya K., Cottle, Brian K., Knighton, Nathan, and Hitchcock, Robert W.
- Owner:
- Brian Cottle
- Based Near Label Tesim:
- Salt Lake City, UT, Utah, United States
- Language:
- English
- Date Uploaded:
- 05/11/2021
- Date Modified:
- 12/08/2023
- Date Created:
- 2019-01-01 to 2019-02-08 and 2020-07-21 to 2020-08-07
- License:
- CC BY NC - Allows others to use and share your data non-commercially and with attribution.
- Resource Type:
- Dataset
- Identifier:
- https://doi.org/10.7278/S50D-FV1Q-BYNS
- Description:
- Light-scattering spectroscopy (LSS) is an established optical approach for nondestructive characterization of biological tissues. Here, we investigated the capabilities of LSS and convolutional neural networks (CNNs) to quantitatively characterize the composition and arrangement of cardiac tissues. We assembled tissue constructs from 200 μm thick sections of fixed myocardium and aortic wall. Thickness of the tissue constructs was similar to the thickness of atrial free wall. In the assembled constructs, the aortic sections represented fibrotic tissue and the depth, volume fraction, and arrangement of these fibrotic insets were varied. We gathered spectra with wavelengths from 500-1100 nm from the constructs at multiple locations relative to a light source. We used single and combinations of two spectra for training of CNNs. With independently measured spectra, we assessed the accuracy of the trained CNNs for classification of tissue constructs from single spectra and combined spectra. In general, classification accuracy with single spectra was smaller than with combined spectra. Combined spectra including spectra from fibers distal from the illumination fiber typically yielded a higher accuracy than proximal single collection fibers. Maximal classification accuracy of depth detection, volume fraction and permutated arrangements was (mean±stddev) 88.97±2.49%, 76.33±1.51% and 84.25±1.88%, respectively. Our studies demonstrate the reliability of quantitative characterization of tissue composition and arrangements using a combination of LSS and CNNs. Potential clinical applications of the developed approach include intraoperative quantification and mapping of atrial fibrosis as well as assessment of ablation lesions.
- Keyword:
- cardiology, neural networks, cardiovascular imaging, heart, spectroscopy, machine learning, and optical imaging
- Subject:
- cardiology
- Creator:
- Hitchcock, Robert W., Sachse, Frank B., Cottle, Brian K., Kelson, Bailey E. B., and Knighton, Nathan J.
- Owner:
- Frank Sachse
- Based Near Label Tesim:
- Salt Lake City, Utah, United States
- Language:
- English
- Date Uploaded:
- 01/09/2020
- Date Modified:
- 10/29/2024
- Date Created:
- 2019-01-01 to 2019-02-08 and 2020-07-21 to 2020-08-07
- License:
- CC BY NC - Allows others to use and share your data non-commercially and with attribution.
- Resource Type:
- Dataset
- Identifier:
- https://doi.org/10.7278/S50D-3Q4J-SC4Y
- Description:
- Localization of the components of the cardiac conduction system (CCS) is essential for many therapeutic procedures in cardiac surgery and interventional cardiology. While histological studies provided fundamental insights into CCS localization, this information is incomplete and difficult to translate to aid in intraprocedural localization. To advance our understanding of CCS localization, we set out to establish a framework for quantifying nodal region morphology. Using this framework, we quantitatively analyzed the sinoatrial node (SAN) and atrioventricular node (AVN) in ovine with menstrual age ranging from 4.4 to 58.3 months. In particular, we studied the SAN and AVN in relation to the epicardial and endocardial surfaces, respectively. Using anatomical landmarks, we excised the nodes and adjacent tissues, sectioned those at a thickness of 4 µm at 100 µm intervals, and applied Masson’s trichrome stain to the sections. These sections were then imaged, segmented to identify nodal tissue, and analyzed to quantify nodal depth and superficial tissue composition. The minimal SAN depth ranged between 20 and 926 µm. AVN minimal depth ranged between 59 and 1192 µm in the AVN extension region, 49 and 980 µm for the compact node, and 148 and 888 µm for the transition to His Bundle region. Using a logarithmic regression model, we found that minimal depth increased logarithmically with age for the AVN (R2=0.818, P=0.002). Also, the myocardial overlay of the AVN was heterogeneous within different regions and decreased with increasing age. Age associated alterations of SAN minimal depth were insignificant. Our study presents examples of characteristic tissue patterns superficial to the AVN and within the SAN. We suggest that the presented framework provides quantitative information for CCS localization. Our studies indicate that procedural methods and localization approaches in regions near the AVN should account for the age of patients in cardiac surgery and interventional cardiology.
- Keyword:
- AVN, sinoatrial node, SAN, atrioventricular node, Masson's Trichrome, cardiology, cardiac conduction system, heart, and physiology
- Subject:
- cardiology and physiology
- Creator:
- Sachse, Frank B., Cottle, Brian K., and Johnson, Jordan
- Owner:
- Frank Sachse
- Language:
- English
- Date Uploaded:
- 11/13/2019
- Date Modified:
- 10/29/2024
- Date Created:
- 2018-01-01 to 2018-12-31
- License:
- CC BY NC - Allows others to use and share your data non-commercially and with attribution.
- Resource Type:
- Dataset
- Identifier:
- https://doi.org/10.7278/S50D-546S-4RVW