An automatic bone segmentation method based on anatomical structure for the knee joint in MDCT image

Meniscus is a fibrocartilaginous anatomical structure that realizes complicated biomechanical functions in the knee joint. However, no comparative morphology studies have been done on different species and conditions regarding the meniscus. Thus, the aim of our pilot study was to compare the morphology of traumatized and aged human and healthy deer meniscus to reveal the tissue ground, growth, degeneration, cell death and inflammation factors. The study included surgery materials from one deer and two humans. Biotin-streptavidin immunohistochemistry was performed for detection of tissue TGFβ1, MMP2, MMP9, collagen I, caspase, Il-1, Il-6, Il-10. The results were evaluated semiquantitatively. An abundant number of Collagen I positive cells were detected in the disordered human meniscus but not in the deer one. TGFβ1 was seen in numerous to abundant number of cells in all the three cases. MMPs and caspase were distributed with numerous to abundant cells in both human and deer meniscus. Numerous to abundant cells of traumatized and aged human menisci showed IL-1 and IL-6, while the deer meniscus demonstrated cytokine expression in a moderate number of cells only in limited zones. The traumatized human meniscus possessed an abundant number of IL-10 positive cells, while the deer and the aged human meniscus showed mainly a moderate number of IL-10 cells with some elevation of cytokine in superficial and deepest layers of the meniscus.

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Whole vertebral bone segmentation method with a statistical intensity-shape model based approach

10.1117/12.878151 ◽

2011 ◽

Cited By ~ 2

Author(s):

Shouhei Hanaoka ◽

Karl Fritscher ◽

Benedikt Schuler ◽

Yoshitaka Masutani ◽

Naoto Hayashi ◽

...

Keyword(s):

Segmentation Method ◽

Bone Segmentation ◽

Shape Model ◽

Vertebral Bone ◽

Model Based

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Six degree of freedom calculation based on principal component analysis for the knee joint in MDCT image

2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011) ◽

10.1109/fuzzy.2011.6007611 ◽

2011 ◽

Cited By ~ 4

Author(s):

Yosuke Uozumi ◽

Kouki Nagamune ◽

Takehiko Matsushita ◽

Seiji Kubo ◽

Ryosuke Kuroda ◽

...

Keyword(s):

Principal Component Analysis ◽

Knee Joint ◽

Principal Component ◽

Component Analysis ◽

Degree Of Freedom ◽

Mdct Image ◽

Six Degree Of Freedom

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Segmentation of wood cell in cross-section using deep convolutional neural networks

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-211386 ◽

2021 ◽

pp. 1-10

Author(s):

Halime Ergun

Keyword(s):

Neural Networks ◽

Cross Section ◽

Convolutional Neural Networks ◽

Optimization Algorithm ◽

Cell Types ◽

Anatomical Structure ◽

Automatic Identification ◽

Segmentation Method ◽

Deep Convolutional Neural Networks ◽

Anatomical Features

Fiber and vessel structures located in the cross-section are anatomical features that play an important role in identifying tree species. In order to determine the microscopic anatomical structure of these cell types, each cell must be accurately segmented. In this study, a segmentation method is proposed for wood cell images based on deep convolutional neural networks. The network, which was developed by combining two-stage CNN structures, was trained using the Adam optimization algorithm. For evaluation, the method was compared with SegNet and U-Net architectures, trained with the same dataset. The losses in these models trained were compared using IoU (Intersection over Union), accuracy, and BF-score measurements on the test data. The automatic identification of the cells in the wood images obtained using a microscope will provide a fast, inexpensive, and reliable tool for those working in this field.

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Variability of anterolateral ligament on MRI images – lack of survey standardization or anatomical variants?

Proceedings of the National Academy of Sciences of Belarus Medical series ◽

10.29235/1814-6023-2021-18-1-58-68 ◽

2021 ◽

Vol 18 (1) ◽

pp. 58-68

Author(s):

O. O. Kostrub ◽

V. V. Кotiuk ◽

Iu. V. Poliachenko ◽

M. A. Gerasimenko ◽

R. I. Blonskyi ◽

...

Keyword(s):

Knee Joint ◽

Layer Structure ◽

Joint Capsule ◽

Anterolateral Ligament ◽

Anatomical Structure ◽

Knee Joints ◽

Anatomical Features ◽

Common Features ◽

Anatomical Element ◽

History Of

The anterolateral ligament is a rotational stabilizer of the knee joint. It is not always clear what we actually see on MRI in the area of anterolateral ligament (ALL).The aim of the study was to evaluate the ALL variants on MRI images to summarize their common features and differences, and to try to find an explanation for the phenomenon of the ALL variability.200 series of MRI images of knee joints were analyzed. The presence of the ALL, the number of its layers, the relation to the joint capsule, and other anatomical features were assessed.The ALL was visualized on MRI at least partially in 88 % of cases. At least partially two-layer structure was detected in 68 % of all 200 MRI series. The wavy appearance of the certain portions of the anterolateral ligament was observed in some normal knee joints without a history of injuries.Determined that the ALL is a separate anatomical element of the knee joint that has a variable, but in most cases two-layered, anatomical structure and can be detected on MRI in at least 88 % of cases. Axial sections help to identify ALL in complex cases and allow analyzing its anatomy, but adding little in the diagnosis of ALL injury.

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