scholarly journals Automatic segmentation of all lower limb muscles from high-resolution magnetic resonance imaging using a cascaded three-dimensional deep convolutional neural network

2019 ◽  
Vol 6 (04) ◽  
pp. 1 ◽  
Author(s):  
Renkun Ni ◽  
Craig H. Meyer ◽  
Silvia S. Blemker ◽  
Joseph M. Hart ◽  
Xue Feng
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Lower Limb ◽  
Automatic Segmentation ◽  
Three Dimensional ◽  
Resonance Imaging ◽  
Lower Limb Muscles

scholarly journals Vertebrobasilar Artery Geometry and Bisilar Artery Plaques: A High-Resolution Magnetic Resonance Imaging Stydy

2020 ◽  
Author(s):  
Jinmei Zheng ◽  
Bin Sun ◽  
Ruolan Lin ◽  
Yongqi Teng ◽  
Xihai Zhao ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
High Resolution ◽  
Large Diameter ◽  
Three Dimensional ◽  
Resonance Imaging ◽  
Logistic Regression Models ◽  
Vertebrobasilar Artery ◽  
Number Of Bends ◽  
The Relationship

Abstract Background Atherosclerotic plaques are often present in regions with complicated flow patterns. Vascular morphology plays a role in hemodynamics. In this study, we investigate the relationship between the geometry of the vertebrobasilar artery system and the basilar artery (BA) plaque prevalence.Methods We enrolled 290 patients with posterior circulation ischemic stroke. We distinguished four configurations of the vertebrobasilar artery: Walking, Tuning Fork, Lambda, and No Confluence. The diameter of the vertebral artery (VA) and the number of bends in the intracranial VA segment was assessed using three-dimensional time-of-flight magnetic resonance angiography. We differentiated between multi-bending (≥ 3 bends) and oligo-bending (< 3 bends) VAs. High-resolution magnetic resonance imaging was used to evaluate BA plaques. Logistic regression models examined the relationship between the geometry type and BA plaque prevalence.Results After adjusting for sex, age, body mass index ≥ 28, hypertension, and diabetes mellitus, the Walking, Lambda, and No Confluence geometries were associated with the presence of BA plaque. Patients with multi-bending VAs in both the Walking (71.43%, P = 0.003) and Lambda group (40.43%, P = 0.018) had more plaques compared to patients with oligo-bending VAs in these groups. In the Lambda group, the diameter difference between the VAs was larger in patients with BA plaques than that in patients without BA plaques (1.4 mm vs. 0.9 mm, P < 0.001).Conclusions The Walking, Lambda, and No Confluence geometry, ≥ 3 bends in the VAs, and a large diameter difference between the VAs were associated with the presence of BA plaque.

scholarly journals Diagnosa Tumor Otak Berdasarkan Citra MRI (Magnetic Resonance Imaging)

2019 ◽  
Vol 18 (2) ◽  
Author(s):  
Ida Bagus Leo Mahadya Suta ◽  
Rukmi Sari Hartati ◽  
Yoga Divayana
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Convolutional Neural Network ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Mri

Tumor otak menjadi salah satu penyakit yang paling mematikan, salah satu jenis yang paling banyak ditemukan adalah glioma sekitar 6 dari 100.000 pasien adalah penderita glioma. Citra digital melalui Magnetic Resonance Imaging (MRI) merupakan salah satu metode untuk membantu dokter dalam menganalisa dan mengklasifikasikan jenis tumor otak. Namun, klasifikasi secara manual membutuhkan waktu yang lama dan memiliki resiko kesalahan yang tinggi, untuk itu dibutuhkan suatu cara otomatis dan akurat dalam melakukan klasifikasi citra MRI. Convolutional Neural Network (CNN) menjadi salah satu solusi dalam melakukan klasifikasi otomatis dalam citra MRI. CNN merupakan algoritma deep learning yang memiliki kemampuan untuk belajar sendiri dari kasus kasus sebelumnya. Dan dari penelitian yang telah dilakukan, diperoleh hasil bahwa CNN mampu dalam menyelesaikan klasifikasi tumor otak dengan akurasi yang tinggi. Peningkatan akurasi diperoleh dengan mengembangkan algoritma CNN baik melalui menentukan nilai kernel dan/atau fungsi aktivasi.

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