scholarly journals Deep Learning Methods for Abnormality Detection and Segmentation in Computed Tomography and Magnetic Resonance Images

2020 ◽  
Author(s):  
Fatemeh Zabihollahy
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Magnetic Resonance Images ◽  
Abnormality Detection ◽  
Learning Methods

scholarly journals Feasibility of Synthetic Computed Tomography Images Generated from Magnetic Resonance Imaging Scans Using Various Deep Learning Methods in the Planning of Radiation Therapy for Prostate Cancer

Cancers ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 40
Author(s):  
Gyu Sang Yoo ◽  
Huan Minh Luu ◽  
Heejung Kim ◽  
Won Park ◽  
Hongryull Pyo ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Computed Tomography ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Soft Tissue ◽  
Ct Images ◽  
Learning Approaches ◽  
Learning Methods ◽  
Computed Tomography Images ◽  
The Mean

We aimed to evaluate and compare the qualities of synthetic computed tomography (sCT) generated by various deep-learning methods in volumetric modulated arc therapy (VMAT) planning for prostate cancer. Simulation computed tomography (CT) and T2-weighted simulation magnetic resonance image from 113 patients were used in the sCT generation by three deep-learning approaches: generative adversarial network (GAN), cycle-consistent GAN (CycGAN), and reference-guided CycGAN (RgGAN), a new model which performed further adjustment of sCTs generated by CycGAN with available paired images. VMAT plans on the original simulation CT images were recalculated on the sCTs and the dosimetric differences were evaluated. For soft tissue, a significant difference in the mean Hounsfield unites (HUs) was observed between the original CT images and only sCTs from GAN (p = 0.03). The mean relative dose differences for planning target volumes or organs at risk were within 2% among the sCTs from the three deep-learning approaches. The differences in dosimetric parameters for D98% and D95% from original CT were lowest in sCT from RgGAN. In conclusion, HU conservation for soft tissue was poorest for GAN. There was the trend that sCT generated from the RgGAN showed best performance in dosimetric conservation D98% and D95% than sCTs from other methodologies.

scholarly journals Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Manan Binth Taj Noor ◽  
Nusrat Zerin Zenia ◽  
M Shamim Kaiser ◽  
Shamim Al Mamun ◽  
Mufti Mahmud
Keyword(s):  
Alzheimer’S Disease ◽  
Parkinson’S Disease ◽  
Alzheimer's Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Neurological Disorders ◽  
Magnetic Resonance Images ◽  
Future Research ◽  
Comparative Performance

Abstract Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided.

AB1161 ARTIFICIAL INTELLIGENCE FOR RHEUMATOLOGY

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1871-1872
Author(s):  
A. C. Genç ◽  
F. Turkoglu Genc ◽  
A. B. Kara ◽  
L. Genc Kaya ◽  
Z. Ozturk ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Sacroiliac Joint ◽  
Axial Skeleton ◽  
Observer Agreement ◽  
Mri Findings ◽  
Learning Methods ◽  
Expert Radiologist ◽  
Active Sacroiliitis

Background:Magnetic resonance imaging (MRI) of sacroiliac (SI) joints is used to detect early sacroiliitis(1). There can be an interobserver disagreement in MRI findings of SI joints of spondyloarthropathy patients between a rheumatologist, a local radiologist, and an expert radiologist(2). Artificial Intelligence and deep learning methods to detect abnormalities have become popular in radiology and other medical fields in recent years(3). Search for “artificial intelligence” and “radiology” in Pubmed for the last five years returned around 1500 clinical studies yet no results were retrieved for “artificial intelligence” and “rheumatology”.Objectives:Artificial Intelligence (AI) can help to detect the pathological area like sacroiliitis or not and also allows us to characterize it as quantitatively rather than qualitatively in the SI-MRI.Methods:Between the years of 2015 and 2019, 8100 sacroiliac MRIs were taken at our center. The MRIs of 1150 patients who were reported as active or chronic sacroiliitis from these sacroiliac MRIs or whose MRIs were considered by the primary physician in favor of sacroiliitis was included in the study. 1441 MRI coronal STIR sequence of 1150 patients were tagged as ‘’active sacroiliitis’’ and trained to detect and localize active sacroiliitis and provide prediction performance. This model is available for various operating systems. (Image1)Results:Precision score, the percentage of sacroiliac images of the trained model, is 87.1%. Recall, the percentage of the total sacroiliac MRIs correctly classified by the model, is 82.1% and the mean average precision (mAP) of the model is 89%.Conclusion:There are gray areas in medicine like sacroiliitis. Inter-observer variability can be reduced by AI and deep learning methods. The efficiency and reliability of health services can be increased in this way.References:[1]Jans L, Egund N, Eshed I, Sudoł-Szopińska I, Jurik AG. Sacroiliitis in Axial Spondyloarthritis: Assessing Morphology and Activity. Semin Musculoskelet Radiol. 2018;22: 180–188.[2]B. Arnbak, T. S. Jensen, C. Manniche, A. Zejden, N. Egund, and A. G. Jurik, “Spondyloarthritis-related and degenerative MRI changes in the axial skeleton—an inter- and intra-observer agreement study,”BMC Musculoskeletal Disorders, vol. 14, article 274, 2013.[3]Rueda, Juan C et al. “Interobserver Agreement in Magnetic Resonance of the Sacroiliac Joints in Patients with Spondyloarthritis.”International journal of rheumatology(2017).Image1.Bilateral active sacroiliitis detected automatically by AI model (in right sacroiliac joint 75.6%> (50%), in left sacroiliac joint 65% (>50%))Disclosure of Interests:None declared

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