BlenderPhotonics - a versatile environment for 3-D complex bio-tissue modeling and light transport simulations based on Blender

Significance: Rapid advances in biophotonics techniques require quantitative, model-based computational approaches to obtain functional and structural information from increasingly complex and multi-scaled anatomies. The lack of efficient tools to accurately model tissue structures and subsequently perform quantitative multi-physics modeling greatly impedes the clinical translation of these modalities. Aim: While the mesh-based Monte Carlo (MMC) method expands our capabilities in simulating complex tissues by using tetrahedral meshes, the generation of such domains often requires specialized meshing tools such as Iso2Mesh. Creating a simplified and intuitive interface for tissue anatomical modeling and optical simulations is essential towards making these advanced modeling techniques broadly accessible to the user community. Approach: We responded to the above challenge by combining the powerful, open-source 3-D modeling software, Blender, with state-of-the-art 3-D mesh generation and MC simulation tools, utilizing the interactive graphical user interface (GUI) in Blender as the front-end to allow users to create complex tissue mesh models, and subsequently launch MMC light simulations. Results: We have developed a Python-based Blender add-on -- BlenderPhotonics -- to interface with Iso2Mesh and MMC, allowing users to create, configure and refine complex simulation domains and run hardware-accelerated 3-D light simulations with only a few clicks. In this tutorial, we provide a comprehensive introduction to this new tool and walk readers through 5 examples, ranging from simple shapes to sophisticated realistic tissue models. Conclusion: BlenderPhotonics is user-friendly and open-source, leveraging the vastly rich ecosystem of Blender. It wraps advanced modeling capabilities within an easy-to-use and interactive interface. The latest software can be downloaded at http://mcx.space/bp.

ANATOMICAL MODELING OF SUPER-, SUB-EYE PATCH AND CHIN OPENINGS IN FRUITS AND NEWBORN

Using modern anatomical methods, 57 preparations of human fetuses 4-10 months and 7 newborns were studied in order to create models of distances of supra-, suborbital and chin openings between themselves and to standard landmarks in fetuses and newborns taking into account their morphometric parameters. The model of the distance from the supraorbital foramen to bregma (Y1): Y1 = β0 + 0.092 x parietal-heel length of the fetus, where β0 :: 2,783, if the age period = 4 months; 3,106 = 5 months; -0.662 = 6 months; 4,728 = 7 months; 2,676 = 8 months; 0.402 = 9 months; -1,727 = 10 months; 9,094 = newborns; model of the distance between the supra- and suborbital foramina (Y2): Y2 = β0 + 0.011 x parietal-heel length of the fetus, where β0 :: 8,147, if the age period = 4 months; 9.086 = 5 months; 10,260 = 6 months; 12,020 = 7 months; 12,129 = 8 months; 15,164 = 9 months; 17,429 = 10 months; 18,808 = newborns; model of the distance between the orbital and chin openings (Y3): Y3 = β0 + 0.002 x parietal-heel length of the fetus, where β0 :: 8.987, if the age period = 4 months; 9,134 = 5 months; 9,892 = 6 months; 12,250 = 7 months; 11,636 = 8 months; 16,755 = 9 months; 17,877 = 10 months; 18,054 = newborns; model of the distance between the chin holes and the lower edge of the mandible (Y4): Y4 = β0 + 0.008 x parietalheel length of the fetus, where β0 :: 0.268, if the age period = 4 months; 0.178 = 5 months; 0.020 = 6 months; -0.152 = 7 months; 0.020 = 8 months; - 0.115 = 9 months; -0.079 = 10 months; -0.039 = newborns; model of the distance between the orbital foramina (Y5): Y5 = β0 + 0.030 x parietal-heel length of the fetus, where β0 :: 5,762, if the age period = 4 months; 5,895 = 5 months; 11,227 = 6 months; 13,793 = 7 months; 11,691 = 8 months; 11,173 = 9 months; 12,633 = 10 months; 14,494 = newborns; model of the distance between the orbital foramina (Y6): Y6 = β0 + 0.008 x parietal-heel length of the fetus, where β0 :: 9,272, if the age period = 4 months; 11,081 = 5 months; 13,467 = 6 months; 16,854 = 7 months; 15,912 = 8 months; 17,653 = 9 months; 22,635 = 10 months; 23,447 = newborns; model of the distance between the chin holes (Y7): Y7 = β0 - 0.014 x parietal-heel length of the fetus, where β0 :: 12,959, if the age period = 4 months; 15,282 = 5 months; 18,117 = 6 months; 23,178 = 7 months; 23,175 = 8 months; 30,496 = 9 months; 32,227 = 10 months; 33,272 = newborns.

Anatomical Modeling of Brain Vasculature in Two-Photon Microscopy by Generalizable Deep Learning

Objective and Impact Statement. Segmentation of blood vessels from two-photon microscopy (2PM) angiograms of brains has important applications in hemodynamic analysis and disease diagnosis. Here, we develop a generalizable deep learning technique for accurate 2PM vascular segmentation of sizable regions in mouse brains acquired from multiple 2PM setups. The technique is computationally efficient, thus ideal for large-scale neurovascular analysis. Introduction. Vascular segmentation from 2PM angiograms is an important first step in hemodynamic modeling of brain vasculature. Existing segmentation methods based on deep learning either lack the ability to generalize to data from different imaging systems or are computationally infeasible for large-scale angiograms. In this work, we overcome both these limitations by a method that is generalizable to various imaging systems and is able to segment large-scale angiograms. Methods. We employ a computationally efficient deep learning framework with a loss function that incorporates a balanced binary-cross-entropy loss and total variation regularization on the network’s output. Its effectiveness is demonstrated on experimentally acquired in vivo angiograms from mouse brains of dimensions up to 808×808×702 μm. Results. To demonstrate the superior generalizability of our framework, we train on data from only one 2PM microscope and demonstrate high-quality segmentation on data from a different microscope without any network tuning. Overall, our method demonstrates 10× faster computation in terms of voxels-segmented-per-second and 3× larger depth compared to the state-of-the-art. Conclusion. Our work provides a generalizable and computationally efficient anatomical modeling framework for brain vasculature, which consists of deep learning-based vascular segmentation followed by graphing. It paves the way for future modeling and analysis of hemodynamic response at much greater scales that were inaccessible before.

Anatomical Modeling of Brain Vasculature in Two-Photon Microscopy by Generalizable Deep Learning

Objective and Impact Statement. Segmentation of blood vessels from two-photon microscopy (2PM) angiograms of brains has important applications in hemodynamic analysis and disease diagnosis. Here, we develop a generalizable deep learning technique for accurate 2PM vascular segmentation of sizable regions in mouse brains acquired from multiple 2PM setups. The technique is computationally efficient, thus ideal for large-scale neurovascular analysis. Introduction. Vascular segmentation from 2PM angiograms is an important first step in hemodynamic modeling of brain vasculature. Existing segmentation methods based on deep learning either lack the ability to generalize to data from different imaging systems or are computationally infeasible for large-scale angiograms. In this work, we overcome both these limitations by a method that is generalizable to various imaging systems and is able to segment large-scale angiograms. Methods. We employ a computationally efficient deep learning framework with a loss function that incorporates a balanced binary-cross-entropy loss and total variation regularization on the network’s output. Its effectiveness is demonstrated on experimentally acquired in vivo angiograms from mouse brains of dimensions up to 808×808×702 μm. Results. To demonstrate the superior generalizability of our framework, we train on data from only one 2PM microscope and demonstrate high-quality segmentation on data from a different microscope without any network tuning. Overall, our method demonstrates 10× faster computation in terms of voxels-segmented-per-second and 3× larger depth compared to the state-of-the-art. Conclusion. Our work provides a generalizable and computationally efficient anatomical modeling framework for brain vasculature, which consists of deep learning-based vascular segmentation followed by graphing. It paves the way for future modeling and analysis of hemodynamic response at much greater scales that were inaccessible before.

ANATOMICAL MODELING OF FETUS AND NEWBORN PALM STRUCTURES

Numerous anomalies manifested in clinical practice, in most cases, can be explained only on the basis of clarifying the origin and interaction of organs and structures, which over time acquire their characteristic forms, having studied their unusual topography and deeply understanding the corresponding embryonic phenomena. A detailed study of the anatomy of the hand is necessary for a correct under-standing of the pathways of the spread of purulent-inflammatory processes and the development of rational methods of surgical treatment. The prognostication models created to foresee standard morphometric parameters of a palm within a perinatal ontogenesis period are: for the length of a palm = β0+ 0.042 х parietal-calcaneal lengths of a fetus, where β0:= 3.587 during the 4th month of gestation; 5.562 = 5th month; 4.071 = 6th month; 4.840 = 7th month; 6.881 = 8th month; 5.624 = 9th month; 5.448 = 10th month; 5.765 = neonates; for the width of a palm = β0+ 0.038 х parietal-calcaneal length of a fetus, where β0= 2,887 for the 4th month of fetal age; 4.341 = 5th month; 2.638 = 6th month; 3.324 = 7th month; 3.548 = 8th month; 1.714 = 9th month; 1.814 = 10th month; 3.231 = neonates. The proposed models of standard morphometric parameters of a palmar aponeurosis in the perinatal ontogenesis period are the following: for its length – the length of a palmar aponeurosis = β0+ 0.022 х parietal-calcaneal length of a fetus, where β0= 3.531 for the 4th month of the gestational age; 6.532 = 5th month; 6.851 = 6th month; 6.526 = 7th month; 7.583 = 8th month; 7.044 = 9th month; 6.964 = 10th month; 7.968 = neonates; for the width – the width of a palmar aponeurosis = β0+ 0.018 х parietal-calcaneal length of a fetus, where β0= 2.624 within the 4th month of a fetus age; 5.431 = 5th age; 3.701 = 6th age; 4.233 = 7th age; 4.121 = 8th month; 3.602 = 9th age; 3.956 = 10th month; 4.881 = neonates.

Anatomical modeling of brain vasculature in two-photon microscopy by generalizable deep learning

Objective and Impact StatementSegmentation of blood vessels from two-photon microscopy (2PM) angiograms of brains has important applications in hemodynamic analysis and disease diagnosis. Here we develop a generalizable deep learning technique for accurate 2PM vascular segmentation of sizable regions in mouse brains acquired from multiple 2PM setups. The technique is computationally efficient, thus ideal for large-scale neurovascular analysis.IntroductionVascular segmentation from 2PM angiograms is an important first step in hemodynamic modeling of brain vasculature. Existing segmentation methods based on deep learning either lack the ability to generalize to data from different imaging systems, or are computationally infeasible for large-scale angiograms. In this work, we overcome both these limitations by a method that is generalizable to various imaging systems, and is able to segment large-scale angiograms.MethodsWe employ a computationally efficient deep learning framework with a loss function that incorporates a balanced binary-cross-entropy loss and a total variation regularization on the network’s output. Its effectiveness is demonstrated on experimentally acquired in-vivo angiograms from mouse brains of dimensions up to 808×808×702 μm.ResultsTo demonstrate the superior generalizability of our framework, we train on data from only one 2PM microscope, and demonstrate high-quality segmentation on data from a different microscope without any network tuning. Overall, our method demonstrates 10× faster computation in terms of voxels-segmented-per-second and 3× larger depth compared to the state-of-the-art.ConclusionOur work provides a generalizable and computationally efficient anatomical modeling framework for brain vasculature, which consists of deep learning based vascular segmentation followed by graphing. It paves the way for future modeling and analysis of hemodynamic response at much greater scales that were inaccessible before.