scholarly journals Cross‐Cohort Automatic Knee MRI Segmentation With Multi‐Planar U‐Nets

2021 ◽  
Author(s):  
Mathias Perslev ◽  
Akshay Pai ◽  
Jos Runhaar ◽  
Christian Igel ◽  
Erik B. Dam
Keyword(s):  
Mri Segmentation ◽  
Knee Mri

scholarly journals The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset

2021 ◽  
Vol 3 (3) ◽  
pp. e200078
Author(s):  
Arjun D. Desai ◽  
Francesco Caliva ◽  
Claudia Iriondo ◽  
Aliasghar Mortazi ◽  
Sachin Jambawalikar ◽  
...  
Keyword(s):  
International Workshop ◽  
Analysis Framework ◽  
Mri Segmentation ◽  
Knee Mri

Can3d: Fast 3d Knee Mri Segmentation Via Compact Context Aggregation

2021 ◽  
Author(s):  
Wei Dai ◽  
Boyeong Woo ◽  
Siyu Liu ◽  
Matthew Marques ◽  
Fangfang Tang ◽  
...  
Keyword(s):  
Mri Segmentation ◽  
Knee Mri

scholarly journals Detecting Small Anatomical Structures in 3D Knee MRI Segmentation by Fully Convolutional Networks

2021 ◽  
Vol 12 (1) ◽  
pp. 283
Author(s):  
Mengtao Sun ◽  
Li Lu ◽  
Ibrahim A. Hameed ◽  
Carl Petter Skaar Kulseng ◽  
Kjell-Inge Gjesdal
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Mri Segmentation ◽  
Anatomical Structures ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Knee Mri ◽  
Magnetic Resonance Imaging Mri ◽  
Relative Structure

Accurately identifying the pixels of small organs or lesions from magnetic resonance imaging (MRI) has a critical impact on clinical diagnosis. U-net is the most well-known and commonly used neural network for image segmentation. However, the small anatomical structures in medical images cannot be well recognised by U-net. This paper explores the performance of the U-net architectures in knee MRI segmentation to find a relative structure that can obtain high accuracies for both small and large anatomical structures. To maximise the utilities of U-net architecture, we apply three types of components, residual blocks, squeeze-and-excitation (SE) blocks, and dense blocks, to construct four variants of U-net, namely U-net variants. Among these variants, our experiments show that SE blocks can improve the segmentation accuracies of small labels. We adopt DeepLabv3plus architecture for 3D medical image segmentation by equipping SE blocks based on this discovery. The experimental results show that U-net with SE block achieves higher accuracy in parts of small anatomical structures. In contrast, DeepLabv3plus with SE block performs better on the average dice coefficient of small and large labels.

Accurate MRI Segmentation of Skull Base Meningiomas

2016 ◽  
Vol 77 (S 02) ◽  
Author(s):  
Francesco Latini ◽  
Elna-Marie Larsson ◽  
Mats Ryttlefors
Keyword(s):  
Skull Base ◽  
Mri Segmentation ◽  
Skull Base Meningiomas

Recent advancements in Fuzzy C-means based techniques for brain MRI Segmentation

2021 ◽  
Vol 16 ◽  
Author(s):  
Ghazanfar Latif ◽  
Jaafar Alghazo ◽  
Fadi N. Sibai ◽  
D.N.F. Awang Iskandar ◽  
Adil H. Khan
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Brain Mri ◽  
Tumor Segmentation ◽  
Brain Anatomy ◽  
Mri Segmentation ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Low Contrast ◽  
Fuzzy C Means

Background: Variations of image segmentation techniques, particularly those used for Brain MRI segmentation, vary in complexity from basic standard Fuzzy C-means (FCM) to more complex and enhanced FCM techniques. Objective: In this paper, a comprehensive review is presented on all thirteen variations of FCM segmentation techniques. In the review process, the concentration is on the use of FCM segmentation techniques for brain tumors. Brain tumor segmentation is a vital step in the process of automatically diagnosing brain tumors. Unlike segmentation of other types of images, brain tumor segmentation is a very challenging task due to the variations in brain anatomy. The low contrast of brain images further complicates this process. Early diagnosis of brain tumors is indeed beneficial to patients, doctors, and medical providers. Results: FCM segmentation works on images obtained from magnetic resonance imaging (MRI) scanners, requiring minor modifications to hospital operations to early diagnose tumors as most, if not all, hospitals rely on MRI machines for brain imaging. In this paper, we critically review and summarize FCM based techniques for brain MRI segmentation.

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