Prostatic urinary tract visualization with super-resolution deep learning models

Yoshimura, Takaaki; Nishioka, Kentaro; Hashimoto, Takayuki; Mori, Takashi; Kogame, S; Seki, Kazuya; Sugimori, Hiroyuki; Yamashina, Hiroko; Nomura, Yusuke; Kato, Fumiki; Kudo, Kohsuke; Shimizu, Satoru; Aoyama, Hidefumi

doi:10.1371/journal.pone.0280076

Cited by 4 publications

(2 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep learning techniques, specifically CNNs, have been applied to various medical image analyses [ 8 , 9 , 10 , 11 ]. CNNs are widely used for image classification [ 12 , 13 , 14 ] regression [ 15 , 16 , 17 ], object detection [ 18 , 19 ], super resolution [ 20 , 21 ], and semantic segmentation [ 22 , 23 , 24 ]. Recent studies have proposed automatic segmentation of the left ventricle lumen to reduce tracing time and interobserver errors in the study of cardiac function [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Left and Right Ventricular Ejection Fractions from cine-MRI Using 3D-CNN

Inomata

Yoshimura

Tang

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

Cardiac function indices must be calculated using tracing from short-axis images in cine-MRI. A 3D-CNN (convolutional neural network) that adds time series information to images can estimate cardiac function indices without tracing using images with known values and cardiac cycles as the input. Since the short-axis image depicts the left and right ventricles, it is unclear which motion feature is captured. This study aims to estimate the indices by learning the short-axis images and the known left and right ventricular ejection fractions and to confirm the accuracy and whether each index is captured as a feature. A total of 100 patients with publicly available short-axis cine images were used. The dataset was divided into training:test = 8:2, and a regression model was built by training with the 3D-ResNet50. Accuracy was assessed using a five-fold cross-validation. The correlation coefficient, MAE (mean absolute error), and RMSE (root mean squared error) were determined as indices of accuracy evaluation. The mean correlation coefficient of the left ventricular ejection fraction was 0.80, MAE was 9.41, and RMSE was 12.26. The mean correlation coefficient of the right ventricular ejection fraction was 0.56, MAE was 11.35, and RMSE was 14.95. The correlation coefficient was considerably higher for the left ventricular ejection fraction. Regression modeling using the 3D-CNN indicated that the left ventricular ejection fraction was estimated more accurately, and left ventricular systolic function was captured as a feature.

show abstract

Section: Introductionmentioning

confidence: 99%

Estimation of Left and Right Ventricular Ejection Fractions from cine-MRI Using 3D-CNN

Inomata

Yoshimura

Tang

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…In recent years, several studies have performed medical image analyses using the deep learning (DL) techniques [4][5][6]. Among the DL techniques using convolutional neural network, classification [7], semantic segmentation [8], and object detection [9] are suitable for medical image analyses, and there are many reports on these techniques.…”

Section: Introductionmentioning

confidence: 99%

Development of Chest X-ray Image Evaluation Software Using the Deep Learning Techniques

et al. 2023

Self Cite

View full text Add to dashboard Cite

Although the widespread use of digital imaging has enabled real-time image display, images in chest X-ray examinations can be confirmed by the radiologist’s eyes. Considering the development of deep learning (DL) technology, its application will make it possible to immediately determine the need for a retake, which is expected to further improve examination throughput. In this study, we developed software for evaluating chest X-ray images to determine whether a repeat radiographic examination is necessary, based on the combined application of DL technologies, and evaluated its accuracy. The target population was 4809 chest images from a public database. Three classification models (CLMs) for lung field defects, obstacle shadows, and the location of obstacle shadows and a semantic segmentation model (SSM) for the lung field regions were developed using a fivefold cross validation. The CLM was evaluated using the overall accuracy in the confusion matrix, the SSM was evaluated using the mean intersection over union (mIoU), and the DL technology-combined software was evaluated using the total response time on this software (RT) per image for each model. The results of each CLM with respect to lung field defects, obstacle shadows, and obstacle shadow location were 89.8%, 91.7%, and 91.2%, respectively. The mIoU of the SSM was 0.920, and the software RT was 3.64 × 10−2 s. These results indicate that the software can immediately and accurately determine whether a chest image needs to be re-scanned.

show abstract

Dataset augmentation with multiple contrasts images in super-resolution processing of T1-weighted brain magnetic resonance images

Kageyama,

Yoshida,

Kondo

et al. 2024

Radiol Phys Technol

View full text Add to dashboard Cite

Prostatic urinary tract visualization with super-resolution deep learning models

Cited by 4 publications

References 31 publications

Estimation of Left and Right Ventricular Ejection Fractions from cine-MRI Using 3D-CNN

Estimation of Left and Right Ventricular Ejection Fractions from cine-MRI Using 3D-CNN

Development of Chest X-ray Image Evaluation Software Using the Deep Learning Techniques

Dataset augmentation with multiple contrasts images in super-resolution processing of T1-weighted brain magnetic resonance images

Contact Info

Product

Resources

About