The Influence of Hindlimb Unloading on the Bone Tissue’s Structure

Baltina, Tatyana; Sachenkov, Oskar; Gerasimov, Oleg V.; Baltin, Maxim; Fedyanin, Artur; Lavrov, Igor

doi:10.1007/s12668-018-0551-2

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…Results for fabric tensor were close to results from previous work [26], and the difference can be explained by dimensions of data (2D via 3D). The second eigenvector of the fabric tensor was almost radial directed (deviation about ± 10 • ), the first eigenvector of the fabric tensor was in the orthogonal direction.…”

Section: Resultssupporting

confidence: 85%

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Automation of Bone Tissue Histology

et al. 2019

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In abstract methods of automation of histology, bone structure is considered. Possible inputs are snapshots from a microscope or computed tomography slices. An algorithm is proposed that differentiates objects according to their color (or grayscale) and recover morphology topology. An algorithm to separate morphological objects by their dimensions and color parameters was built. Measured parameters were bone surface, bone area, porosity, cortical thickness, canal number, canal area, and etc. Additionally, we measured the anisotropy properties of the bone tissue: distribution of porosity direction and degree of porosity elongation. A bone example was scanned by computed tomography. All data were measured by the proposed method and the results presented. An example algorithm of work on computed tomography data is shown in this work.

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Section: Resultssupporting

confidence: 85%

“…To calculate the degree of anisotropy, a fabric tensor was used [23][24][25][26]. For this purpose, all data should meshed by 2-D Cartesian grid and fabric tensor should be calculated for every element.…”

Section: Macro Scale Datamentioning

confidence: 99%

Automation of Bone Tissue Histology

et al. 2019

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“…Segmentation of ROIs is an important task for BAA. MRI and X-ray have different features that are used for BAA that includes local histograms [ 14 , 15 ], image textures [ 16 , 17 ], and structure tensor eigenvalues [ 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Bone Age Assessment Empowered with Deep Learning: A Survey, Open Research Challenges and Future Directions

et al. 2020

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Deep learning is a quite useful and proliferating technique of machine learning. Various applications, such as medical images analysis, medical images processing, text understanding, and speech recognition, have been using deep learning, and it has been providing rather promising results. Both supervised and unsupervised approaches are being used to extract and learn features as well as for the multi-level representation of pattern recognition and classification. Hence, the way of prediction, recognition, and diagnosis in various domains of healthcare including the abdomen, lung cancer, brain tumor, skeletal bone age assessment, and so on, have been transformed and improved significantly by deep learning. By considering a wide range of deep-learning applications, the main aim of this paper is to present a detailed survey on emerging research of deep-learning models for bone age assessment (e.g., segmentation, prediction, and classification). An enormous number of scientific research publications related to bone age assessment using deep learning are explored, studied, and presented in this survey. Furthermore, the emerging trends of this research domain have been analyzed and discussed. Finally, a critical discussion section on the limitations of deep-learning models has been presented. Open research challenges and future directions in this promising area have been included as well.

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Bone Age Classification Using CNN Algorithm

Ravinuthala,

2024

2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS)

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The Influence of Hindlimb Unloading on the Bone Tissue’s Structure

Cited by 5 publications

References 13 publications

Automation of Bone Tissue Histology

Automation of Bone Tissue Histology

Bone Age Assessment Empowered with Deep Learning: A Survey, Open Research Challenges and Future Directions

Bone Age Classification Using CNN Algorithm

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