Development of Combination Methods for Detecting Malignant Uptakes Based on Physiological Uptake Detection Using Object Detection With PET-CT MIP Images

Automatic Aortic Valve Extraction Using Deep Learning with Contrast-Enhanced Cardiac CT Images

PURPOSE

This study evaluates the use of deep learning techniques to automatically extract and delineate the aortic valve annulus region from contrast-enhanced cardiac CT images. Two approaches, namely, segmentation and object detection, were compared to determine their accuracy.

MATERIALS AND METHODS

A dataset of 32 contrast-enhanced cardiac CT scans was analyzed. The segmentation approach utilized the DeepLabv3+ model, while the object detection approach employed YOLOv2. The dataset was augmented through rotation and scaling, and five-fold cross-validation was applied. The accuracy of both methods was evaluated using the Dice similarity coefficient (DSC), and their performance in estimating the aortic valve annulus area was compared.

RESULTS

The object detection approach achieved a mean DSC of 0.809, significantly outperforming the segmentation approach, which had a mean DSC of 0.711. Object detection also demonstrated higher precision and recall, with fewer false positives and negatives. The aortic valve annulus area estimation had a mean error of 2.55 mm.

CONCLUSIONS

Object detection showed superior performance in identifying the aortic valve annulus region, suggesting its potential for clinical application in cardiac imaging. The results highlight the promise of deep learning in improving the accuracy and efficiency of preoperative planning for cardiovascular interventions.

The montage method improves the classification of suspected acute ischemic stroke using the convolution neural network and brain MRI

This study investigated the usefulness of the montage method that combines four different magnetic resonance images into one images for automatic acute ischemic stroke (AIS) diagnosis with deep learning method. The montage image was consisted from diffusion weighted image (DWI), fluid attenuated inversion recovery (FLAIR), arterial spin labeling (ASL), and apparent diffusion coefficient (ASL). The montage method was compared with pseudo color map (pCM) which was consisted from FLAIR, ASL and ADC. 473 AIS patients were classified into four categories: mechanical thrombectomy, conservative therapy, hemorrhage, and other diseases. The results showed that the montage image significantly outperformed pCM in terms of accuracy (montage image = 0.76 ± 0.01, pCM = 0.54 ± 0.05) and the area under the curve (AUC) (montage image = 0.94 ± 0.01, pCM = 0.76 ± 0.01). This study demonstrates the usefulness of the montage method and its potential for overcoming the limitations of pCM.

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

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.

Fundamentals of Bowel Cancer for Biomedical Engineers

Bowel cancer is a multifactorial disease arising from a combination of genetic predisposition and environmental factors. Detection of bowel cancer and its precursor lesions is predominantly performed by either visual inspection of the colonic mucosa during endoscopy or cross-sectional imaging. Most cases are diagnosed when the cancer is already at an advanced stage. These modalities are less reliable for detecting lesions at the earliest stages, when they are typically small or flat. Removal of lesions at the earliest possible stage reduces the risk of cancer death, which is largely due to a reduced risk of subsequent metastasis. In this review, we summarised the origin of bowel cancer and the mechanism of its metastasis. In particular, we reviewed a broad spectrum of literatures covering the biomechanics of bowel cancer and its measurement techniques that are pertinent to the successful development of a bowel cancer diagnostic device. We also reviewed existing bowel cancer diagnostic techniques that are available for clinical use. Finally, we outlined current clinical needs and highlighted the potential roles of medical robotics on early bowel cancer diagnosis.

Artificial intelligence for nuclear medicine in oncology

As in all other medical fields, artificial intelligence (AI) is increasingly being used in nuclear medicine for oncology. There are many articles that discuss AI from the viewpoint of nuclear medicine, but few focus on nuclear medicine from the viewpoint of AI. Nuclear medicine images are characterized by their low spatial resolution and high quantitativeness. It is noted that AI has been used since before the emergence of deep learning. AI can be divided into three categories by its purpose: (1) assisted interpretation, i.e., computer-aided detection (CADe) or computer-aided diagnosis (CADx). (2) Additional insight, i.e., AI provides information beyond the radiologist's eye, such as predicting genes and prognosis from images. It is also related to the field called radiomics/radiogenomics. (3) Augmented image, i.e., image generation tasks. To apply AI to practical use, harmonization between facilities and the possibility of black box explanations need to be resolved.

AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging:: Towards Radiophenomics

Artificial intelligence (AI) techniques have significant potential to enable effective, robust, and automated image phenotyping including the identification of subtle patterns. AI-based detection searches the image space to find the regions of interest based on patterns and features. There is a spectrum of tumor histologies from benign to malignant that can be identified by AI-based classification approaches using image features. The extraction of minable information from images gives way to the field of "radiomics" and can be explored via explicit (handcrafted/engineered) and deep radiomics frameworks. Radiomics analysis has the potential to be used as a noninvasive technique for the accurate characterization of tumors to improve diagnosis and treatment monitoring. This work reviews AI-based techniques, with a special focus on oncological PET and PET/CT imaging, for different detection, classification, and prediction/prognosis tasks. We also discuss needed efforts to enable the translation of AI techniques to routine clinical workflows, and potential improvements and complementary techniques such as the use of natural language processing on electronic health records and neuro-symbolic AI techniques.

A Comparative Evaluation of Computed Tomography Images for the Classification of Spirometric Severity of the Chronic Obstructive Pulmonary Disease with Deep Learning

Recently, deep learning applications in medical imaging have been widely applied. However, whether it is sufficient to simply input the entire image or whether it is necessary to preprocess the setting of the supervised image has not been sufficiently studied. This study aimed to create a classifier trained with and without preprocessing for the Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification using CT images and to evaluate the classification accuracy of the GOLD classification by confusion matrix. According to former GOLD 0, GOLD 1, GOLD 2, and GOLD 3 or 4, eighty patients were divided into four groups (n = 20). The classification models were created by the transfer learning of the ResNet50 network architecture. The created models were evaluated by confusion matrix and AUC. Moreover, the rearranged confusion matrix for former stages 0 and ≥1 was evaluated by the same procedure. The AUCs of original and threshold images for the four-class analysis were 0.61 ± 0.13 and 0.64 ± 0.10, respectively, and the AUCs for the two classifications of former GOLD 0 and GOLD ≥ 1 were 0.64 ± 0.06 and 0.68 ± 0.12, respectively. In the two-class classification by threshold image, recall and precision were over 0.8 in GOLD ≥ 1, and in the McNemar–Bowker test, there was some symmetry. The results suggest that the preprocessed threshold image can be possibly used as a screening tool for GOLD classification without pulmonary function tests, rather than inputting the normal image into the convolutional neural network (CNN) for CT image learning.