Toward automatic reformation at the orbitomeatal line in head computed tomography using object detection algorithm

Ichikawa, Shota; Itadani, Hideki; Sugimori, Hiroyuki

doi:10.1007/s13246-022-01153-z

Cited by 5 publications

(3 citation statements)

References 32 publications

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“…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

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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.

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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

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“…In particular, it is particularly helpful for image classi cation tasks, where highly complex anatomical differences are considered. Moreover, the utility of deep learning has been extended to the early diagnosis of disorders with high accuracy by improving object detection [8,9] , segmentation [10] , and image reconstruction [11][12][13][14][15] performance. Recent studies have demonstrated the usefulness of deep learning algorithms in diagnosis based on computed tomography (CT) imaging, determination of disease progression, and even estimating recommended recipes with appropriate doses in the regular treatment of COPD.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based computed tomography assessment for lung function prediction in chronic obstructive pulmonary disease

Shimizu,

Sugimori,

Tanabe

et al. 2023

Preprint

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Deep learning models based on medical imaging enable numerical functional predictions in combination with regression methods. In this study, we evaluate the prediction performance of a deep learning-based model for the raw value and percent predicted forced expiratory volume in one second (FEV1) in patients with chronic obstructive pulmonary disease (COPD). To this end, ResNet50-based regression prediction models were constructed for FEV1 and %FEV1 based on 200 CT scans. 10-fold cross-validation was performed to yield ten models in aggregate. The prediction model for %FEV1 was externally validated using 20 data points. Two hundred internal CT datasets were assessed using commercial software, producing a regression model predicting airway [%WA] and parenchymal indices [%LAV]. The average Root Mean Squared Error(RMSE) value of the 10 predictive models was 627.65 for FEV1 as per internal validation and 15.34 for %FEV1. The externally validated RMSE for %FEV1 was 11.52, whereas that for %FEV1 was 23.18. The predictive model for %FEV1 yielded significant positive correlations corresponding to both internal and external validation. The proposed models exhibited better prediction accuracy for %FEV1 than for FEV1. Further studies are required to improve the accuracy further and determine the validity of longitudinal applications.

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“…Another approach utilizes an object detection algorithm to automatically reformat head CT images based on the orbitomeatal line [18]. Despite its benefits, this method mainly focuses on the automatic reformatting of axial head CT images, with limited involvement in 3D reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Deep learning for lung cancer on computed tomography: early detection and prognostic prediction

Zheng¹

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Three-dimensional (3D) reconstruction of head Computed Tomography (CT) images elucidates the intricate spatial relationships of tissue structures, thereby assisting in accurate diagnosis. Nonetheless, securing an optimal head CT scan without deviation is challenging in clinical settings, owing to poor positioning by technicians, patient's physical constraints, or CT scanner tilt angle restrictions. Manual formatting and reconstruction not only introduce subjectivity but also strain time and labor resources. To address these issues, we propose an efficient automatic head CT images 3D reconstruction method, improving accuracy and repeatability, as well as diminishing manual intervention. Our approach employs a deep learningbased object detection algorithm, identifying and evaluating orbitomeatal line landmarks to automatically reformat the images prior to reconstruction. Given the dearth of existing evaluations of object detection algorithms in the context of head CT images, we compared ten methods from both theoretical and experimental perspectives. By exploring their precision, efficiency, and robustness, we singled out the lightweight YOLOv8 as the aptest algorithm for our task, with an mAP of 92.91% and impressive robustness against class imbalance. Our qualitative evaluation of standardized reconstruction results demonstrates the clinical practicability and validity of our method.

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Toward automatic reformation at the orbitomeatal line in head computed tomography using object detection algorithm

Cited by 5 publications

References 32 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

Deep learning-based computed tomography assessment for lung function prediction in chronic obstructive pulmonary disease

Deep learning for lung cancer on computed tomography: early detection and prognostic prediction

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