scholarly journals Improving Quantitative Magnetic Resonance Imaging Using Deep Learning

2020 ◽  
Vol 24 (04) ◽  
pp. 451-459
Author(s):  
Fang Liu
Keyword(s):  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Quantitative Mri ◽  
Resonance Imaging ◽  
Tissue Segmentation ◽  
Learning Methods ◽  
Musculoskeletal Tissue ◽  
Conventional Machine ◽  
Magnetic Resonance Imaging Mri

AbstractDeep learning methods have shown promising results for accelerating quantitative musculoskeletal (MSK) magnetic resonance imaging (MRI) for T2 and T1ρ relaxometry. These methods have been shown to improve musculoskeletal tissue segmentation on parametric maps, allowing efficient and accurate T2 and T1ρ relaxometry analysis for monitoring and predicting MSK diseases. Deep learning methods have shown promising results for disease detection on quantitative MRI with diagnostic performance superior to conventional machine-learning methods for identifying knee osteoarthritis.

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.

scholarly journals Deep Learning with Limited Data: Organ Segmentation Performance by U-Net

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1199
Author(s):  
Michelle Bardis ◽  
Roozbeh Houshyar ◽  
Chanon Chantaduly ◽  
Alexander Ushinsky ◽  
Justin Glavis-Bloom ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Data Availability ◽  
Resonance Imaging ◽  
Learning Network ◽  
Transrectal Biopsy ◽  
Minimum Number ◽  
Magnetic Resonance Imaging Mri ◽  
Deep Learning Network

(1) Background: The effectiveness of deep learning artificial intelligence depends on data availability, often requiring large volumes of data to effectively train an algorithm. However, few studies have explored the minimum number of images needed for optimal algorithmic performance. (2) Methods: This institutional review board (IRB)-approved retrospective review included patients who received prostate magnetic resonance imaging (MRI) between September 2014 and August 2018 and a magnetic resonance imaging (MRI) fusion transrectal biopsy. T2-weighted images were manually segmented by a board-certified abdominal radiologist. Segmented images were trained on a deep learning network with the following case numbers: 8, 16, 24, 32, 40, 80, 120, 160, 200, 240, 280, and 320. (3) Results: Our deep learning network’s performance was assessed with a Dice score, which measures overlap between the radiologist’s segmentations and deep learning-generated segmentations and ranges from 0 (no overlap) to 1 (perfect overlap). Our algorithm’s Dice score started at 0.424 with 8 cases and improved to 0.858 with 160 cases. After 160 cases, the Dice increased to 0.867 with 320 cases. (4) Conclusions: Our deep learning network for prostate segmentation produced the highest overall Dice score with 320 training cases. Performance improved notably from training sizes of 8 to 120, then plateaued with minimal improvement at training case size above 160. Other studies utilizing comparable network architectures may have similar plateaus, suggesting suitable results may be obtainable with small datasets.

Magnetic Resonance Imaging of Articular Cartilage within the Knee

2018 ◽  
Vol 31 (02) ◽  
pp. 155-165 ◽  
Author(s):  
Alissa Burge ◽  
Hollis Potter ◽  
Erin Argentieri
Keyword(s):  
Magnetic Resonance Imaging ◽  
Articular Cartilage ◽  
Magnetic Resonance ◽  
Cartilage Repair ◽  
Quantitative Mri ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Mri ◽  
Biochemical Assessment ◽  
Insight Into ◽  
Repair Techniques

AbstractMagnetic resonance imaging (MRI) provides an effective and noninvasive means by which to evaluate articular cartilage within the knee. Existing techniques can be utilized to detect and monitor longitudinal changes in cartilage status due to injury or progression of degenerative disease. Quantitative MRI (qMRI) techniques can provide a metric by which to evaluate the efficacy of cartilage repair techniques and offer insight into the composition of cartilage and cartilage repair tissue. In this review, we provide background on MR signal generation and decay, the utility of morphologic MRI assessment, and qMRI techniques for the biochemical assessment of cartilage (dGEMRIC, T2, T2*, T1ρ, sodium, gagCEST). Finally, the description and utility of these qMRI techniques for the evaluation of cartilage repair are discussed.

Antiuterotrophic effects of a pure antioestrogen, ICI 182,780: magnetic resonance imaging of the uterus in ovariectomized monkeys

1992 ◽  
Vol 135 (2) ◽  
pp. 239-NP ◽  
Author(s):  
M. Dukes ◽  
D. Miller ◽  
A. E. Wakeling ◽  
J. C. Waterton
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Quantitative Mri ◽  
Macaca Nemestrina ◽  
Dependent Manner ◽  
Resonance Imaging ◽  
Ici 182,780 ◽  
Uterine Involution ◽  
Magnetic Resonance Imaging Mri ◽  
Long Acting

ABSTRACT ICI 182,780 is a potent specific pure antioestrogen which may offer advantages in breast cancer treatment compared with partial agonists like tamoxifen. To characterize further the potency and efficacy of ICI 182,780, its effects on the uterus of ovariectomized, oestrogen-treated monkeys (Macaca nemestrina) have been measured using magnetic resonance imaging (MRI). Quantitative MRI allows accurate non-invasive repetitive measurements of endometrial and myometrial volume following hormonal treatments, using each animal as its own control. Single i.m. injections of a long-acting oil-based formulation of ICI 182,780 sustained blockade of oestradiol action on the monkey uterus in a dose-dependent manner for 3–6 weeks. Repeated injections of 4 mg ICI 182,780/kg at 4-weekly intervals provided increasingly effective blockade of uterine proliferation. In a short-acting formulation, ICI 182,780 also completely blocked the trophic action of oestradiol, administered concurrently, in ovariectomized monkeys. Similarly, ICI 182,780 caused involution of the uterus stimulated by prior treatment with oestradiol. The rate and extent of uterine involution in monkeys treated with ICI 182,780 was similar to that seen following oestrogen withdrawal. These studies demonstrate that ICI 182,780 is a fully effective pure antioestrogen in a primate. Journal of Endocrinology (1992) 135, 239–247

scholarly journals Brain Tumor Classification using Deep Learning Algorithms

2021 ◽  
Vol 9 (12) ◽  
pp. 417-426
Author(s):  
Ankita Kadam
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Neural Networks ◽  
Deep Learning ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Tumor Classification ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Mri

Abstract: A Brain tumor is one aggressive disease. An estimated more than 84,000 people will receive a primary brain tumor diagnosis in 2021 and an estimated 18,600 people will die from a malignant brain tumor (brain cancer) in 2021.[8] The best technique to detect brain tumors is by using Magnetic Resonance Imaging (MRI). More than any other cancer, brain tumors can have lasting and life-altering physical, cognitive, and psychological impacts on a patient’s life and hence faster diagnosis and best treatment plan should be devised to improve the life expectancy and well-being of these patients. Neural networks have shown colossal accuracy in image classification and segmentation problems. In this paper, we propose comparative studies of various deep learning models based on different types of Neural Networks(ANN, CNN, TL) to firstly identify brain tumors and then classify them into Benign Tumor, Malignant Tumor or Pituitary Tumor. The data set used holds 3190 images on T1-weighted contrast-enhanced images which were cleaned and augmented. The best ANN model concluded with an accuracy of 78% and the best CNN model consisting of 3 convolution layers had an accuracy of 90%. The VGG16(retrained on the dataset) model surpasses other ANN, CNN, TL models for multi-class tumor classification. This proposed network achieves significantly better performance with a validation accuracy of 94% and an F1-Score of 91. Keywords: Artificial Neural Network(ANN), Convolution Neural Network (CNN), Transfer Learning(TL), Magnetic Resonance Imaging(MRI.)

scholarly journals Development of brain tumor segmentation of magnetic resonance imaging (MRI) using U-Net deep learning

2021 ◽  
Vol 4 (9(112)) ◽  
pp. 23-31
Author(s):  
Wasan M. Jwaid ◽  
Zainab Shaker Matar Al-Husseini ◽  
Ahmad H. Sabry
Keyword(s):  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Tumor Segmentation ◽  
Resonance Imaging ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

Brain tumors are the growth of abnormal cells or a mass in a brain. Numerous kinds of brain tumors were discovered, which need accurate and early detection techniques. Currently, most diagnosis and detection methods rely on the decision of neuro-specialists and radiologists to evaluate brain images, which may be time-consuming and cause human errors. This paper proposes a robust U-Net deep learning Convolutional Neural Network (CNN) model that can classify if the subject has a tumor or not based on Brain Magnetic resonance imaging (MRI) with acceptable accuracy for medical-grade application. The study built and trained the 3D U-Net CNN including encoding/decoding relationship architecture to perform the brain tumor segmentation because it requires fewer training images and provides more precise segmentation. The algorithm consists of three parts; the first part, the downsampling part, the bottleneck part, and the optimum part. The resultant semantic maps are inserted into the decoder fraction to obtain the full-resolution probability maps. The developed U-Net architecture has been applied on the MRI scan brain tumor segmentation dataset in MICCAI BraTS 2017. The results using Matlab-based toolbox indicate that the proposed architecture has been successfully evaluated and experienced for MRI datasets of brain tumor segmentation including 336 images as training data and 125 images for validation. This work demonstrated comparative performance and successful feasibility of implementing U-Net CNN architecture in an automated framework of brain tumor segmentations in Fluid-attenuated inversion recovery (FLAIR) MR Slices. The developed U-Net CNN model succeeded in performing the brain tumor segmentation task to classify the input brain images into a tumor or not based on the MRI dataset.

Quantitative magnetic resonance imaging for focal liver lesions: bridging the gap between research and clinical practice

2021 ◽  
pp. 20210220
Author(s):  
Roberto Cannella ◽  
Riccardo Sartoris ◽  
Jules Grégory ◽  
Lorenzo Garzelli ◽  
Valérie Vilgrain ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Clinical Practice ◽  
Magnetic Resonance ◽  
Quantitative Mri ◽  
Focal Liver Lesions ◽  
Liver Lesions ◽  
Resonance Imaging ◽  
Apparent Diffusion ◽  
Magnetic Resonance Imaging Mri ◽  
Diagnostic Work

Magnetic resonance imaging (MRI) is highly important for the detection, characterization, and follow-up of focal liver lesions. Several quantitative MRI-based methods have been proposed in addition to qualitative imaging interpretation to improve the diagnostic work-up and prognostics in patients with focal liver lesions. This includes DWI with apparent diffusion coefficient measurements, intravoxel incoherent motion, perfusion imaging, MR elastography, and radiomics. Multiple research studies have reported promising results with quantitative MRI methods in various clinical settings. Nevertheless, applications in everyday clinical practice are limited. This review describes the basic principles of quantitative MRI-based techniques and discusses the main current applications and limitations for the assessment of focal liver lesions.

scholarly journals Using deep learning to predict temporomandibular joint disc perforation based on magnetic resonance imaging

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jae-Young Kim ◽  
Dongwook Kim ◽  
Kug Jin Jeon ◽  
Hwiyoung Kim ◽  
Jong-Ki Huh
Keyword(s):  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Random Forest ◽  
Temporomandibular Joint ◽  
Prediction Models ◽  
Superior Performance ◽  
Resonance Imaging ◽  
Temporomandibular Joint Disc ◽  
Magnetic Resonance Imaging Mri

AbstractThe goal of this study was to develop a deep learning-based algorithm to predict temporomandibular joint (TMJ) disc perforation based on the findings of magnetic resonance imaging (MRI) and to validate its performance through comparison with previously reported results. The study objects were obtained by reviewing medical records from January 2005 to June 2018. 299 joints from 289 patients were divided into perforated and non-perforated groups based on the existence of disc perforation confirmed during surgery. Experienced observers interpreted the TMJ MRI images to extract features. Data containing those features were applied to build and validate prediction models using random forest and multilayer perceptron (MLP) techniques, the latter using the Keras framework, a recent deep learning architecture. The area under the receiver operating characteristic (ROC) curve (AUC) was used to compare the performances of the models. MLP produced the best performance (AUC 0.940), followed by random forest (AUC 0.918) and disc shape alone (AUC 0.791). The MLP and random forest were also superior to previously reported results using MRI (AUC 0.808) and MRI-based nomogram (AUC 0.889). Implementing deep learning showed superior performance in predicting disc perforation in TMJ compared to conventional methods and previous reports.

scholarly journals Segmentasi Tumor Otak Berdasarkan Citra Magnetic Resonance Imaging Dengan Menggunakan Metode U-NET

2020 ◽  
Vol 19 (2) ◽  
pp. 151
Author(s):  
Ida Bagus Leo Mahadya Suta ◽  
Made Sudarma ◽  
I Nyoman Satya Kumara
Keyword(s):  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Learning Rate ◽  
Batch Size ◽  
Resonance Imaging ◽  
Dice Coefficient ◽  
Magnetic Resonance Imaging Mri

Tumor otak merupakan salah satu penyakit yang mematikan dimana 3.7% per 100.000 pasien mengidap tumor ganas. Untuk menganalisa tumor otak dapat dilakukan melalui segmentasi citra Magnetic Resonance Imaging (MRI). Proses analisa citra secara otomatis dibutuhkan untuk menghemat waktu dan meningkatkan akurasi dari diagnosa yang dilakukan. Segmentasi secara otomatis dapat dilakukan dengan deep learning. U-NET merupakan salah satu metode yang digunakan untuk melakukan segmentasi citra medis karena bekerja dapa pixel level. Dengan menerapkan fungsi aktivasi ReLU dan Adam Optimizer, metode ini dapat menyelesaikan permasalahan segmentasi tumor otak. Dataset untuk proses training dan validation menggunakan BRATS 2017. Beberapa hyperparameter diterapkan pada metode ini yaitu, learning rate (lr) = 0.0001, batch size (bz) = 5, epoch = 80 dan beta (  ) = 0.9. Dari serangkaian proses yang dilakukan, akurasi metode U-NET dihitung dengan rumus Dice Coefficient dan menghasilkan nilai akurasi sebagai berikut: 90.22% (Full Tumor), 78.09% (Core Tumor) dan 80.20% (Enhancing Tumor).

Sign in / Sign up

Export Citation Format

Share Document