OCIF: automatically learning the optimized clinical information fusion method for computer-aided diagnosis tasks.

PURPOSE: In computer-aided diagnosis, the fusion of image features extracted from neural networks and clinical information is crucial to improve diagnostic accuracy. How to integrate low-dimensional clinical information (LDCF) with high-dimensional network features (HDNF) is an urgent problem to be solved. We offer a new network search framework to address this problem, which can provide optimized LDCF fusion and efficient dimensionality reduction in HDNF. METHODS: OCIF innovatively uses Gaussian process optimization to explore the search space for the number of fully connected (FC) layers, the number of neurons in each FC layer, the activation function, the dropout factor, and whether to add clinical information to each FC layer. Moreover, OCIF employs transfer learning to reduce the training parameter space and improve search efficiency. To evaluate the effectiveness of the proposed OCIF, we utilized three popular end-to-end overall survival (OS) time prediction models to predict the three classes. RESULTS: Our experimental results show that applying OCIF to a classical computer-aided diagnosis neural network can improve classification accuracy. Experiments on the 2020 BRATS dataset prove that OCIF achieves satisfactory performance, with an accuracy of 0.684, precision of 0.735, recall of 0.684, and F1-score of 0.675 on the OS time prediction task. CONCLUSION: OCIF effectively and creatively combines clinical information and network features, leveraging both clinical information and image features to enhance the accuracy of the final diagnosis. Our experiments demonstrate that the use of OCIF can significantly improve computer-aided diagnosis accuracy, and the approach has the potential to be extended to other medical classification tasks as well.

BP-Net: Boundary and perfusion feature guided dual-modality ultrasound video analysis network for fibrous cap integrity assessment.

Ultrasonography is one of the main imaging methods for monitoring and diagnosing atherosclerosis due to its non-invasiveness and low-cost. Automatic differentiation of carotid plaque fibrous cap integrity by using multi-modal ultrasound videos has significant diagnostic and prognostic value for cardiovascular and cerebrovascular disease patients. However, the task faces several challenges, including high variation in plaque location and shape, the absence of analysis mechanism focusing on fibrous cap, the lack of effective mechanism to capture the relevance among multi-modal data for feature fusion and selection, etc. To overcome these challenges, we propose a new target boundary and perfusion feature guided video analysis network (BP-Net) based on conventional B-mode ultrasound and contrast-enhanced ultrasound videos for assessing the integrity of fibrous cap. Based on our previously proposed plaque auto-tracking network, in our BP-Net, we further introduce the plaque edge attention module and reverse mechanism to focus the dual video analysis on the fiber cap of plaques. Moreover, to fully explore the rich information on the fibrous cap and inside/outside of the plaque, we propose a feature fusion module for B-mode and contrast video to filter out the most valuable features for fibrous cap integrity assessment. Finally, multi-head convolution attention is proposed and embedded into transformer-based network, which captures semantic features and global context information to obtain accurate evaluation of fibrous caps integrity. The experimental results demonstrate that the proposed method has high accuracy and generalizability with an accuracy of 92.35% and an AUC of 0.935, which outperforms than the state-of-the-art deep learning based methods. A series of comprehensive ablation studies suggest the effectiveness of each proposed component and show great potential in clinical application.

Prediction of programmed cell death protein 1 in hepatocellular carcinoma patients using radiomics analysis with radiofrequency-based ultrasound multifeature maps.

BACKGROUND: This study explored the feasibility of radiofrequency (RF)-based radiomics analysis techniques for the preoperative prediction of programmed cell death protein 1 (PD-1) in patients with hepatocellular carcinoma (HCC). METHODS: The RF-based radiomics analysis method used ultrasound multifeature maps calculated from the RF signals of HCC patients, including direct energy attenuation (DEA) feature map, skewness of spectrum difference (SSD) feature map, and noncentrality parameter S of the Rician distribution (NRD) feature map. From each of the above ultrasound maps, 345 high-throughput radiomics features were extracted. Then, the useful radiomics features were selected by the sparse representation method and input into support vector machine (SVM) classifier for PD-1 prediction. RESULTS AND CONCLUSION: Among all the RF-based prediction models and the ultrasound grayscale comparative model, the RF-based model using all of the three ultrasound feature maps had the highest prediction accuracy (ACC) and area under the curve (AUC), which were 92.5% and 94.23%, respectively. The method proposed in this paper is effective for the meaningful feature extraction of RF signals and can effectively predict PD-1 in patients with HCC.

Automatic multi-plaque tracking and segmentation in ultrasonic videos.

Carotid plaque tracking and segmentation in ultrasound videos is the premise for subsequent plaque property evaluation and treatment plan development. However, the task is quite challenging, as it needs to address the problems of poor image quality, plaque shape variations among frames, the existence of multiple plaques, etc. To overcome these challenges, we propose a new automatic multi-plaque tracking and segmentation (AMPTS) framework. AMPTS consists of three modules. The first module is a multi-object detector, in which a Dual Attention U-Net is proposed to detect multiple plaques and vessels simultaneously. The second module is a set of single-object trackers that can utilize the previous tracking results efficiently and achieve stable tracking of the current target by using channel attention and a ranking strategy. To make the first module and the second module work together, a parallel tracking module based on a simplified 'tracking-by-detection' mechanism is proposed to solve the challenge of tracking object variation. Extensive experiments are conducted to compare the proposed method with several state-of-the-art deep learning based methods. The experimental results demonstrate that the proposed method has high accuracy and generalizability with a Dice similarity coefficient of 0.83 which is 0.16, 0.06 and 0.27 greater than MAST (Lai et al., 2020), Track R-CNN (Voigtlaender et al., 2019) and VSD (Yang et al., 2019) respectively and has made significant improvements on seven other indicators. In the additional Testing set 2, our method achieved a Dice similarity coefficient of 0.80, an accuracy of 0.79, a precision of 0.91, a Recall 0.70, a F1 score of 0.79, an AP@0.5 of 0.92, an AP@0.7 of 0.74, and an expected average overlap of 0.79. Numerous ablation studies suggest the effectiveness of each proposed component and the great potential for multiple carotid plaques tracking and segmentation in clinical practice.

Preoperative Prediction of Microvascular Invasion of Hepatocellular Carcinoma: Radiomics Algorithm Based on Ultrasound Original Radio Frequency Signals.

Background: To evaluate the accuracy of radiomics algorithm based on original radio frequency (ORF) signals for prospective prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) lesions. Methods: In this prospective study, we enrolled 42 inpatients diagnosed with HCC from January 2018 to December 2018. All HCC lesions were proved by surgical resection and histopathology results, including 21 lesions with MVI. Ultrasound ORF data and grayscale ultrasound images of HCC lesions were collected before operation for further radiomics analysis. Three ultrasound feature maps were calculated using signal analysis and processing (SAP) technology in first feature extraction. The diagnostic accuracy of model based on ORF signals was compared with the model based on grayscale ultrasound images. Results: A total of 1,050 radiomics features were extracted from ORF signals of each HCC lesion. The performance of MVI prediction model based on ORF was better than those based on grayscale ultrasound images. The best area under curve, accuracy, sensitivity, and specificity of ultrasound radiomics in prediction of MVI were 95.01, 92.86, 85.71, and 100%, respectively. Conclusions: Radiomics algorithm based on ultrasound ORF data combined with SAP technology can effectively predict MVI, which has potential clinical application value for non-invasively preoperative prediction of MVI in HCC patients.