The Experts below are selected from a list of 312 Experts worldwide ranked by ideXlab platform
Adam C. Martin - One of the best experts on this subject based on the ideXlab platform.
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Structural Redundancy in Supracellular Actomyosin Networks Enables Robust Tissue Folding
Developmental cell, 2019Co-Authors: Hannah G. Yevick, Pearson Miller, Jörn Dunkel, Adam C. MartinAbstract:Summary Tissue morphogenesis is strikingly robust. Yet, how tissues are sculpted under challenging conditions is unknown. Here, we combined network analysis, experimental perturbations, and computational modeling to determine how network connectivity between hundreds of contractile cells on the ventral side of the Drosophila embryo ensures robust tissue folding. We identified two network properties that mechanically promote robustness. First, redundant supracellular cytoskeletal network paths ensure global connectivity, even with network degradation. By forming many more connections than are required, morphogenesis is not disrupted by local network damage, analogous to the way Redundancy guarantees the large-scale function of vasculature and transportation networks. Second, directional stiffening of edges oriented orthogonal to the folding axis promotes furrow formation at lower contractility levels. Structural Redundancy and directional network stiffening ensure robust tissue folding with proper orientation.
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Structural Redundancy in supracellular actomyosin networks enables robust tissue folding
2019Co-Authors: Hannah G. Yevick, Pearson Miller, Jörn Dunkel, Adam C. MartinAbstract:Tissue morphogenesis is strikingly reproducible. Yet, how tissues are robustly sculpted, even under challenging conditions, is unknown. Here, we combined network analysis, experimental perturbations, and computational modeling to determine how network connectivity between hundreds of contractile cells on the ventral side of the Drosophila embryo ensures robust tissue folding. We identified two network properties that mechanically promote robustness. First, redundant supracellular cytoskeletal network paths ensure global connectivity, even with network degradation. By forming many more connections than are required, morphogenesis is not disrupted by local network damage, analogous to the way Redundancy guarantees the large-scale function of vasculature and transportation networks. Second, directional stiffening of edges oriented orthogonal to the folding axis promotes furrow formation at lower contractility levels. Structural Redundancy and directional network stiffening ensure robust tissue folding with proper orientation.
Ryo Kawasaki - One of the best experts on this subject based on the ideXlab platform.
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iternet retinal image segmentation utilizing Structural Redundancy in vessel networks
Workshop on Applications of Computer Vision, 2020Co-Authors: Manisha Verma, Yuta Nakashima, Hajime Nagahara, Ryo KawasakiAbstract:Retinal vessel segmentation is of great interest for diagnosis of retinal vascular diseases. To further improve the performance of vessel segmentation, we propose IterNet, a new model based on UNet [1], with the ability to find obscured details of the vessel from the segmented vessel image itself, rather than the raw input image. IterNet consists of multiple iterations of a mini-UNet, which can be 4× deeper than the common UNet. IterNet also adopts the weight-sharing and skip-connection features to facilitate training; therefore, even with such a large architecture, IterNet can still learn from merely 10~20 labeled images, without pre-training or any prior knowledge. IterNet achieves AUCs of 0.9816, 0.9851, and 0.9881 on three mainstream datasets, namely DRIVE, CHASE-DB1, and STARE, respectively, which currently are the best scores in the literature. The source code is available1.
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WACV - IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks
2020 IEEE Winter Conference on Applications of Computer Vision (WACV), 2020Co-Authors: Manisha Verma, Yuta Nakashima, Hajime Nagahara, Ryo KawasakiAbstract:Retinal vessel segmentation is of great interest for diagnosis of retinal vascular diseases. To further improve the performance of vessel segmentation, we propose IterNet, a new model based on UNet [1], with the ability to find obscured details of the vessel from the segmented vessel image itself, rather than the raw input image. IterNet consists of multiple iterations of a mini-UNet, which can be 4× deeper than the common UNet. IterNet also adopts the weight-sharing and skip-connection features to facilitate training; therefore, even with such a large architecture, IterNet can still learn from merely 10~20 labeled images, without pre-training or any prior knowledge. IterNet achieves AUCs of 0.9816, 0.9851, and 0.9881 on three mainstream datasets, namely DRIVE, CHASE-DB1, and STARE, respectively, which currently are the best scores in the literature. The source code is available1.
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IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks
arXiv: Image and Video Processing, 2019Co-Authors: Manisha Verma, Yuta Nakashima, Hajime Nagahara, Ryo KawasakiAbstract:Retinal vessel segmentation is of great interest for diagnosis of retinal vascular diseases. To further improve the performance of vessel segmentation, we propose IterNet, a new model based on UNet, with the ability to find obscured details of the vessel from the segmented vessel image itself, rather than the raw input image. IterNet consists of multiple iterations of a mini-UNet, which can be 4$\times$ deeper than the common UNet. IterNet also adopts the weight-sharing and skip-connection features to facilitate training; therefore, even with such a large architecture, IterNet can still learn from merely 10$\sim$20 labeled images, without pre-training or any prior knowledge. IterNet achieves AUCs of 0.9816, 0.9851, and 0.9881 on three mainstream datasets, namely DRIVE, CHASE-DB1, and STARE, respectively, which currently are the best scores in the literature. The source code is available.
Hannah G. Yevick - One of the best experts on this subject based on the ideXlab platform.
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Structural Redundancy in Supracellular Actomyosin Networks Enables Robust Tissue Folding
Developmental cell, 2019Co-Authors: Hannah G. Yevick, Pearson Miller, Jörn Dunkel, Adam C. MartinAbstract:Summary Tissue morphogenesis is strikingly robust. Yet, how tissues are sculpted under challenging conditions is unknown. Here, we combined network analysis, experimental perturbations, and computational modeling to determine how network connectivity between hundreds of contractile cells on the ventral side of the Drosophila embryo ensures robust tissue folding. We identified two network properties that mechanically promote robustness. First, redundant supracellular cytoskeletal network paths ensure global connectivity, even with network degradation. By forming many more connections than are required, morphogenesis is not disrupted by local network damage, analogous to the way Redundancy guarantees the large-scale function of vasculature and transportation networks. Second, directional stiffening of edges oriented orthogonal to the folding axis promotes furrow formation at lower contractility levels. Structural Redundancy and directional network stiffening ensure robust tissue folding with proper orientation.
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Structural Redundancy in supracellular actomyosin networks enables robust tissue folding
2019Co-Authors: Hannah G. Yevick, Pearson Miller, Jörn Dunkel, Adam C. MartinAbstract:Tissue morphogenesis is strikingly reproducible. Yet, how tissues are robustly sculpted, even under challenging conditions, is unknown. Here, we combined network analysis, experimental perturbations, and computational modeling to determine how network connectivity between hundreds of contractile cells on the ventral side of the Drosophila embryo ensures robust tissue folding. We identified two network properties that mechanically promote robustness. First, redundant supracellular cytoskeletal network paths ensure global connectivity, even with network degradation. By forming many more connections than are required, morphogenesis is not disrupted by local network damage, analogous to the way Redundancy guarantees the large-scale function of vasculature and transportation networks. Second, directional stiffening of edges oriented orthogonal to the folding axis promotes furrow formation at lower contractility levels. Structural Redundancy and directional network stiffening ensure robust tissue folding with proper orientation.
Manisha Verma - One of the best experts on this subject based on the ideXlab platform.
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iternet retinal image segmentation utilizing Structural Redundancy in vessel networks
Workshop on Applications of Computer Vision, 2020Co-Authors: Manisha Verma, Yuta Nakashima, Hajime Nagahara, Ryo KawasakiAbstract:Retinal vessel segmentation is of great interest for diagnosis of retinal vascular diseases. To further improve the performance of vessel segmentation, we propose IterNet, a new model based on UNet [1], with the ability to find obscured details of the vessel from the segmented vessel image itself, rather than the raw input image. IterNet consists of multiple iterations of a mini-UNet, which can be 4× deeper than the common UNet. IterNet also adopts the weight-sharing and skip-connection features to facilitate training; therefore, even with such a large architecture, IterNet can still learn from merely 10~20 labeled images, without pre-training or any prior knowledge. IterNet achieves AUCs of 0.9816, 0.9851, and 0.9881 on three mainstream datasets, namely DRIVE, CHASE-DB1, and STARE, respectively, which currently are the best scores in the literature. The source code is available1.
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WACV - IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks
2020 IEEE Winter Conference on Applications of Computer Vision (WACV), 2020Co-Authors: Manisha Verma, Yuta Nakashima, Hajime Nagahara, Ryo KawasakiAbstract:Retinal vessel segmentation is of great interest for diagnosis of retinal vascular diseases. To further improve the performance of vessel segmentation, we propose IterNet, a new model based on UNet [1], with the ability to find obscured details of the vessel from the segmented vessel image itself, rather than the raw input image. IterNet consists of multiple iterations of a mini-UNet, which can be 4× deeper than the common UNet. IterNet also adopts the weight-sharing and skip-connection features to facilitate training; therefore, even with such a large architecture, IterNet can still learn from merely 10~20 labeled images, without pre-training or any prior knowledge. IterNet achieves AUCs of 0.9816, 0.9851, and 0.9881 on three mainstream datasets, namely DRIVE, CHASE-DB1, and STARE, respectively, which currently are the best scores in the literature. The source code is available1.
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IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks
arXiv: Image and Video Processing, 2019Co-Authors: Manisha Verma, Yuta Nakashima, Hajime Nagahara, Ryo KawasakiAbstract:Retinal vessel segmentation is of great interest for diagnosis of retinal vascular diseases. To further improve the performance of vessel segmentation, we propose IterNet, a new model based on UNet, with the ability to find obscured details of the vessel from the segmented vessel image itself, rather than the raw input image. IterNet consists of multiple iterations of a mini-UNet, which can be 4$\times$ deeper than the common UNet. IterNet also adopts the weight-sharing and skip-connection features to facilitate training; therefore, even with such a large architecture, IterNet can still learn from merely 10$\sim$20 labeled images, without pre-training or any prior knowledge. IterNet achieves AUCs of 0.9816, 0.9851, and 0.9881 on three mainstream datasets, namely DRIVE, CHASE-DB1, and STARE, respectively, which currently are the best scores in the literature. The source code is available.
Tony M Keaveny - One of the best experts on this subject based on the ideXlab platform.
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vertebral fragility and Structural Redundancy
Journal of Bone and Mineral Research, 2012Co-Authors: Aaron J Fields, Shashank Nawathe, Senthil K Eswaran, Michael G Jekir, Mark F Adams, Panayiotis Papadopoulos, Tony M KeavenyAbstract:The mechanisms of age-related vertebral fragility remain unclear, but may be related to the degree of “Structural Redundancy” of the vertebra; ie, its ability to safely redistribute stress internally after local trabecular failure from an isolated mechanical overload. To better understand this issue, we performed biomechanical testing and nonlinear micro-CT–based finite element analysis on 12 elderly human thoracic ninth vertebral bodies (age 76.9 ± 10.8 years). After experimentally overloading the vertebrae to measure strength, we used nonlinear finite element analysis to estimate the amount of failed tissue and understand the failure mechanisms. We found that the amount of failed tissue per unit bone mass decreased with decreasing bone volume fraction (r2 = 0.66, p < 0.01). Thus, for the weak vertebrae with low bone volume fraction, overall failure of the vertebra occurred after failure of just a tiny proportion of the bone tissue (<5%). This small proportion of failed tissue had two sources: the existence of fewer vertically oriented load paths to which load could be redistributed from failed trabeculae; and the vulnerability of the trabeculae in these few load paths to undergo bending-type failure mechanisms, which further weaken the bone. Taken together, these characteristics suggest that diminished Structural Redundancy may be an important aspect of age-related vertebral fragility: vertebrae with low bone volume fraction are highly susceptible to collapse because so few trabeculae are available for load redistribution if the external loads cause any trabeculae to fail. © 2012 American Society for Bone and Mineral Research.
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Vertebral fragility and Structural Redundancy.
Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research, 2012Co-Authors: Aaron J Fields, Shashank Nawathe, Senthil K Eswaran, Michael G Jekir, Mark F Adams, Panayiotis Papadopoulos, Tony M KeavenyAbstract:The mechanisms of age-related vertebral fragility remain unclear, but may be related to the degree of “Structural Redundancy” of the vertebra; ie, its ability to safely redistribute stress internally after local trabecular failure from an isolated mechanical overload. To better understand this issue, we performed biomechanical testing and nonlinear micro-CT–based finite element analysis on 12 elderly human thoracic ninth vertebral bodies (age 76.9 ± 10.8 years). After experimentally overloading the vertebrae to measure strength, we used nonlinear finite element analysis to estimate the amount of failed tissue and understand the failure mechanisms. We found that the amount of failed tissue per unit bone mass decreased with decreasing bone volume fraction (r2 = 0.66, p
