Recognition of Thyroid Ultrasound Standard Plane Images Based on Residual Network

Detection of fetal facial anatomy in standard ultrasonographic sections based on real-time target detection network

At present, prenatal ultrasound is one of the important means for screening fetal malformations. In the process of prenatal ultrasound diagnosis, the accurate recognition of fetal facial ultrasound standard plane is crucial for facial malformation detection and disease screening. Due to the dense distribution of fetal facial images, no obvious structure contour boundary, small structure area, and large area overlap in the middle of the structure detection frame, this paper regards the fetal facial standard plane and its structure recognition as a universal target detection task for the first time, and applies real-time YOLO v5s to the fetal facial ultrasound standard plane structure detection and classification task. First, we detect the structure of a single slice, and take the structure of a slice class as the recognition object. Second, we carry out structural detection experiments on three standard planes; then, on the basis of the previous stage, the images of all parts included in the ultrasound examination of multiple fetuses were collected. In the single-class structure detection experiment and the structure detection and classification experiment of three types of standard planes, the overall recognition effect of Precision and Recall index data is better, with Precision being 98.3% and 98.1%, and Recall being 99.3% and 98.2%, respectively. The experimental results show that the model has the ability to identify fetal facial anatomy and standard sections in different data, which can help the physician to automatically and quickly screen out the standard sections of each fetal facial ultrasound.

Development and Clinical Application of Artificial Intelligence Assistant System for Rotator Cuff Ultrasound Scanning

OBJECTIVE

We developed an intelligent assistance system for shoulder ultrasound imaging, incorporating deep-learning algorithms to facilitate standard plane recognition and automatic tissue segmentation of the rotator cuff and its surrounding structures. We evaluated the system's performance using a dedicated data set of rotator cuff ultrasound images to assess its feasibility in clinical practice.

METHODS

To fulfill the system's primary functions, we designed a standard plane recognition module based on the ResNet50 network and an automatic tissue segmentation module using the Mask R-CNN model. The modules were trained on carefully curated data sets. The standard plane recognition module automatically identifies a specific standard plane based on the ultrasound image characteristics. The automatic tissue segmentation module effectively delineates and segments anatomical structures within the identified standard plane.

RESULTS

With the use of 59,265 shoulder joint ultrasound images, the standard plane recognition model achieved an impressive recognition accuracy of 94.9% in the test set, with an average precision rate of 96.4%, recall rate of 95.4% and F1 score of 95.9%. The automatic tissue segmentation model, tested on 1886 images, exhibited a commendable average intersection over union value of 96.2%, indicating robustness and accuracy. The model achieved mean intersection over union values exceeding 90.0% for all standard planes, indicating its effectiveness in precisely delineating the anatomical structures.

CONCLUSION

Our shoulder joint musculoskeletal intelligence system swiftly and accurately identifies standard planes and performs automatic tissue segmentation.

Ultra-Attention: Automatic Recognition of Liver Ultrasound Standard Sections Based on Visual Attention Perception Structures

Acquisition of a standard section is a prerequisite for ultrasound diagnosis. For a long time, there has been a lack of clear definitions of standard liver views because of physician experience. The accurate automated scanning of standard liver sections, however, remains one of ultrasonography medicine's most important issues. In this article, we enrich and expand the classification criteria of liver ultrasound standard sections from clinical practice and propose an Ultra-Attention structured perception strategy to automate the recognition of these sections. Inspired by the attention mechanism in natural language processing, the standard liver ultrasound views will participate in the global attention algorithm as modular local images in computer vision of ultrasound images, which will significantly amplify small features that would otherwise go unnoticed. In addition to using the dropout mechanism, we also use a Part-Transfer Learning training approach to fine-tune the model's rate of convergence to increase its robustness. The proposed Ultra-Attention model outperforms various traditional convolutional neural network-based techniques, achieving the best known performance in the field with a classification accuracy of 93.2%. As part of the feature extraction procedure, we also illustrate and compare the convolutional structure and the Ultra-Attention approach. This analysis provides a reasonable view for future research on local modular feature capture in ultrasound images. By developing a standard scan guideline for liver ultrasound-based illness diagnosis, this work will advance the research on automated disease diagnosis that is directed by standard sections of liver ultrasound.

Early Pregnancy Fetal Facial Ultrasound Standard Plane-Assisted Recognition Algorithm

OBJECTIVES

Ultrasound screening during early pregnancy is vital in preventing congenital disabilities. For example, nuchal translucency (NT) thickening is associated with fetal chromosomal abnormalities, particularly trisomy 21 and fetal heart malformations. Obtaining accurate ultrasound standard planes of a fetal face during early pregnancy is the key to subsequent biometry and disease diagnosis. Therefore, we propose a lightweight target detection network for early pregnancy fetal facial ultrasound standard plane recognition and quality assessment.

METHODS

First, a clinical control protocol was developed by ultrasound experts. Second, we constructed a YOLOv4 target detection algorithm based on the backbone network as GhostNet and added attention mechanisms CBAM and CA to the backbone and neck structure. Finally, key anatomical structures in the image were automatically scored according to a clinical control protocol to determine whether they were standard planes.

RESULTS

We reviewed other detection techniques and found that the proposed method performed well. The average recognition accuracy for six structures was 94.16%, the detection speed was 51 FPS, and the model size was 43.2 MB, and a reduction of 83% compared with the original YOLOv4 model was obtained. The precision for the standard median sagittal plane was 97.20%, and the accuracy for the standard retro-nasal triangle view was 99.07%.

CONCLUSIONS

The proposed method can better identify standard or non-standard planes from ultrasound image data, providing a theoretical basis for automatic acquisition of standard planes in the prenatal diagnosis of early pregnancy fetuses.

A Deep-Learning-Based Artificial Intelligence System for the Pathology Diagnosis of Uterine Smooth Muscle Tumor

We aimed to develop an artificial intelligence (AI) diagnosis system for uterine smooth muscle tumors (UMTs) by using deep learning. We analyzed the morphological features of UMTs on whole-slide images (233, 108, and 30 digital slides of leiomyosarcomas, leiomyomas, and smooth muscle tumors of uncertain malignant potential stained with hematoxylin and eosin, respectively). Aperio ImageScope software randomly selected ≥10 areas of the total field of view. Pathologists randomly selected a marked region in each section that was no smaller than the total area of 10 high-power fields in which necrotic, vascular, collagenous, and mitotic areas were labeled. We constructed an automatic identification algorithm for cytological atypia and necrosis by using ResNet and constructed an automatic detection algorithm for mitosis by using YOLOv5. A logical evaluation algorithm was then designed to obtain an automatic UMT diagnostic aid that can "study and synthesize" a pathologist's experience. The precision, recall, and F1 index reached more than 0.920. The detection network could accurately detect the mitoses (0.913 precision, 0.893 recall). For the prediction ability, the AI system had a precision of 0.90. An AI-assisted system for diagnosing UMTs in routine practice scenarios is feasible and can improve the accuracy and efficiency of diagnosis.

Deep Transfer Learning-Based Breast Cancer Detection and Classification Model Using Photoacoustic Multimodal Images

The rapid development of technologies in biomedical research has enriched and broadened the range of medical equipment. Magnetic resonance imaging, ultrasonic imaging, and optical imaging have been discovered by diverse research communities to design multimodal systems, which is essential for biomedical applications. One of the important tools is photoacoustic multimodal imaging (PAMI) which combines the concepts of optics and ultrasonic systems. At the same time, earlier detection of breast cancer becomes essential to reduce mortality. The recent advancements of deep learning (DL) models enable detection and classification the breast cancer using biomedical images. This article introduces a novel social engineering optimization with deep transfer learning-based breast cancer detection and classification (SEODTL-BDC) model using PAI. The intention of the SEODTL-BDC technique is to detect and categorize the presence of breast cancer using ultrasound images. Primarily, bilateral filtering (BF) is applied as an image preprocessing technique to remove noise. Besides, a lightweight LEDNet model is employed for the segmentation of biomedical images. In addition, residual network (ResNet-18) model can be utilized as a feature extractor. Finally, SEO with recurrent neural network (RNN) model, named SEO-RNN classifier, is applied to allot proper class labels to the biomedical images. The performance validation of the SEODTL-BDC technique is carried out using benchmark dataset and the experimental outcomes pointed out the supremacy of the SEODTL-BDC approach over the existing methods.