scholarly journals Machine learning to automatically detect anatomical landmarks on phase-contrast enhanced magnetic resonance angiography

2021 ◽  
Vol 22 (Supplement_2) ◽  
Author(s):  
A Guala ◽  
M Mejia Cordova ◽  
X Morales ◽  
G Jimenez-Perez ◽  
L Dux-Santoy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Aortic Valve ◽  
Phase Contrast ◽  
Learning Algorithm ◽  
Anatomical Landmarks ◽  
Anatomic Landmarks ◽  
Sinotubular Junction ◽  
Contrast Enhanced ◽  
Fully Automatic ◽  
Level Performance

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Guala A. received funding from the Spanish Ministry of Science, Innovation and Universities Introduction The heterogeneous characteristic of the thoracic aorta implies that all biomarkers with potential for risk stratification need to be references to a specific location. This is the case, for example, of diameter [1], stiffness [2] and wall shear stress [3]. This is normally achieved by the manual identification of a limited number of key anatomic landmarks [4], which is a time-demanding task and may impact biomarkers accuracy and reproducibility. Automatic identification of these anatomic landmarks may speed-up the analysis and allow for the creation of fully automatic image analysis pipelines. Machine learning (ML) algorithms might be suitable for this task. Purpose The aim of this study was to test the performance of a ML algorithm in localizing key thoracic anatomical landmarks on phase-contrast enhanced magnetic resonance angiograms (PC-MRA). Methods PC-MRA of 323 patients with native aorta and aortic valve and a variety of aortic conditions (141 bicuspid aortic valve patients, 60 patients with degenerative aortic aneurysms, 82 patients with genetic aortopathy and 40 healthy volunteers) were included in this study. Four anatomical landmarks were manually identified on PC-MRA by an experienced researcher: sinotubular junction, the pulmonary artery bifurcation and the first and third supra-aortic vessel braches. A reinforcement learning algorithm (DQN), combining Q-learning with deep neural networks, was trained. The algorithm was tested in a separate set of 30 PC-MRA with similar distribution of aortic conditions in which human intra-observer reproducibility was quantified. The distance between points was used as quality metric and human annotation was considered as ground-truth. Repeated-measures ANOVA was used for statistical testing. Results ML algorithm resulted in performance similar to the intra-observer variability obtained by the experienced human reader in the identification of the sinotubular junction (11.1 ± 8.6 vs 11.0 ± 8.1 mm, p = 0.949) and first (6.8 ± 5.6 vs 6.6 ± 3.9 mm, p = 0.886) and third (8.4 ± 7.4 vs 6.8 ± 4.0 mm, p = 0.161) supra-aortic vessels branches. However, the algorithm did not reach human-level performance in the localization of the pulmonary artery bifurcation (15.2 ± 13.1 vs 10.2 ± 7.0 mm, p = 0.008). The time needed to the ML algorithm to locate all points ranged between 0.8 and 1.6 seconds on a standard computer while manual annotation required around two minutes to be performed. Conclusions The rapid identification of key aortic anatomical landmarks by a reinforced learning algorithm is feasible with human-level performance. This approach may thus be used for the design of fully-automatic pipeline for 4D flow CMR analysis.

scholarly journals Reinforcement machine learning-based aortic anatomical landmarks detection from phase-contrast enhanced magnetic resonance angiography

2021 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
M Mejia Cordova ◽  
A Guala ◽  
X Morales ◽  
G Jimenez-Perez ◽  
L Dux-Santoy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Magnetic Resonance ◽  
Medical Imaging ◽  
Magnetic Resonance Angiography ◽  
Phase Contrast ◽  
Learning Algorithm ◽  
Aortic Disease ◽  
Imaging Data ◽  
4D Flow ◽  
Contrast Enhanced

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Spanish Ministry of Science, Innovation and Universities; La Marató de TV3 Introduction Automatic analysis of medical imaging data may improve their clinical impact by reducing analysis time and improving reproducibility. Many medical imaging data, like 4D-flow magnetic resonance imaging (MRI), are often quantified regionally, implying the need for anatomical landmark identification to locate correspondences in the extracted data and compare among patients. Machine learning (ML) techniques hold potential for automatic analysis of medical imaging. Phase-contrast enhanced magnetic resonance angiography (PC-MRA) is a class of angiograms not requiring the administration of contrast agents. Purpose We aimed to test whether a machine learning algorithm can be trained to identify key anatomical cardiovascular landmarks on PC-MRA images and compare its performance with humans. Methods Three-hundred twenty-three aortic PC-MRA were manually annotated with the location of 4 landmarks: sinotubular junction, pulmonary artery bifurcation and first and third supra-aortic vessels (Figure 1), often used to separate the aorta in sub-regions. Patients included in the training dataset comprised healthy volunteers (40), bicuspid aortic valve patients (141), patients with degenerative aortic disease (60) and patients with genetically-triggered aortic disease (82), all without previous aortic surgery and with native aortic valve. PC-MRA images and manual annotations were used to train a DQN, a reinforcement learning algorithm that combines Q-learning with deep neural networks. The agents can navigate the images and optimally find the landmarks by following the policies learned during training. Data from thirty patients, distributed in terms of aortic condition as the training set, unseen by the algorithm in the training phase, were used to quantify intra-observer reproducibility and to assess ML algorithm performance. Distance between points was used as metric for comparisons, original human annotation was used as ground-truth and repeated-measures ANOVA was used for statistical testing. Results Human and machine learning performed similarly in the identification of the sinotubular junction (distance between points of 11.0 ± 8.1 vs. 11.1 ± 8.6 mm, respectively, p = 0.949) and first (6.6 ± 3.9 vs. 6.8 ± 5.6 mm, p = 0.886) and third (6.8 ± 4.0 vs. 8.4 ± 7.4 mm, p = 0.161) supra-aortic vessels branches but human annotation outperformed ML landmark detection in the identification of the pulmonary artery bifurcation (10.2 ± 7.0 vs. 15.2 ± 13.1 mm, p = 0.008). Computation time for landmark detection by ML was between 0.8 and 1.6 seconds on a standard computer while human annotation took approximatively two minutes. Conclusions ML-based aortic landmarks detection from phase-contrast enhanced magnetic resonance angiography is feasible and fast and performs similarly to human. Reinforced learning anatomical landmark identification unlock automatic extraction of a variety of regional aortic data, including complex 4D flow parameters. Abstract Figure

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Design of English text-to-speech conversion algorithm based on machine learning

2020 ◽  
pp. 1-12
Author(s):  
Li Dongmei
Keyword(s):  
Machine Learning ◽  
Speech Synthesis ◽  
Feature Recognition ◽  
Learning Algorithm ◽  
Morphological Structure ◽  
English Text ◽  
Text To Speech ◽  
Part Of Speech ◽  
Modern Computer ◽  
Conversion Algorithm

English text-to-speech conversion is the key content of modern computer technology research. Its difficulty is that there are large errors in the conversion process of text-to-speech feature recognition, and it is difficult to apply the English text-to-speech conversion algorithm to the system. In order to improve the efficiency of the English text-to-speech conversion, based on the machine learning algorithm, after the original voice waveform is labeled with the pitch, this article modifies the rhythm through PSOLA, and uses the C4.5 algorithm to train a decision tree for judging pronunciation of polyphones. In order to evaluate the performance of pronunciation discrimination method based on part-of-speech rules and HMM-based prosody hierarchy prediction in speech synthesis systems, this study constructed a system model. In addition, the waveform stitching method and PSOLA are used to synthesize the sound. For words whose main stress cannot be discriminated by morphological structure, label learning can be done by machine learning methods. Finally, this study evaluates and analyzes the performance of the algorithm through control experiments. The results show that the algorithm proposed in this paper has good performance and has a certain practical effect.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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