A Two-stage Diagnostic Framework for Post-ablation Treatment Response Assessment in Patients with Hepatocellular Carcinoma.

Contrast-enhanced ultrasound (CEUS) video plays an important role in post-ablation treatment response assessment in patients with hepatocellular carcinoma (HCC). However, the assessment of treatment response using CEUS video is challenging due to issues such as high inter-frame data repeatability, small ablation area and poor imaging quality of CEUS video. To address these issues, we propose a two-stage diagnostic framework for post-ablation treatment response assessment in patients with HCC using CEUS video. The first stage is a location stage, which is used to locate the ablation area. At this stage, we propose a Yolov5-SFT to improve the location results of the ablation area and a similarity comparison module (SCM) to reduce data repeatability. The second stage is an assessment stage, which is used for the evaluation of postoperative efficacy. At this stage, we design an EfficientNet-SK to improve assessment accuracy. The Experimental results on the self-collected data show that the proposed framework outperforms other selected algorithms, and can effectively assist doctors in the assessment of post-ablation treatment response.

Multi-task learning for quality assessment of fetal head ultrasound images.

It is essential to measure anatomical parameters in prenatal ultrasound images for the growth and development of the fetus, which is highly relied on obtaining a standard plane. However, the acquisition of a standard plane is, in turn, highly subjective and depends on the clinical experience of sonographers. In order to deal with this challenge, we propose a new multi-task learning framework using a faster regional convolutional neural network (MF R-CNN) architecture for standard plane detection and quality assessment. MF R-CNN can identify the critical anatomical structure of the fetal head and analyze whether the magnification of the ultrasound image is appropriate, and then performs quality assessment of ultrasound images based on clinical protocols. Specifically, the first five convolution blocks of the MF R-CNN learn the features shared within the input data, which can be associated with the detection and classification tasks, and then extend to the task-specific output streams. In training, in order to speed up the different convergence of different tasks, we devise a section train method based on transfer learning. In addition, our proposed method also uses prior clinical and statistical knowledge to reduce the false detection rate. By identifying the key anatomical structure and magnification of the ultrasound image, we score the ultrasonic plane of fetal head to judge whether it is a standard image or not. Experimental results on our own-collected dataset show that our method can accurately make a quality assessment of an ultrasound plane within half a second. Our method achieves promising performance compared with state-of-the-art methods, which can improve the examination effectiveness and alleviate the measurement error caused by improper ultrasound scanning.

Role of acoustic radiation force impulse imaging elastography in the assessment of steatohepatitis and fibrosis in rat models.

Acoustic radiation force impulse (ARFI) elastography is a non-invasive method for performing liver assessment via liver shear wave velocity (SWV) measurements. The aim of this study was to evaluate the performance of the ARFI technique in the diagnosis of nonalcoholic steatohepatitis (NASH) and fibrosis and to investigate the effect of steatosis and inflammation on liver fibrosis SWV measurements in a rat model of nonalcoholic fatty liver disease (NAFLD). The ex vivo right liver lobes from 110 rats were processed and embedded in a fabricated gelatin phantom, and the other lobes were used for histologic assessment. The SWV induced by acoustic radiation force was derived to evaluate liver stiffness. The experimental results showed that the liver SWV value could be used to differentiate non-NASH rats from NASH-presenting rats and NASH from cirrhosis, and these comparisons showed areas under the receiver operating characteristic curves (AUROC) of 0.951 and 0.980, respectively. The diagnostic performances of ARFI elastography in predicting severe fibrosis (F ≥ 3) and cirrhosis (F ≥ 4) showed AUROC values of 0.997 and 0.993, respectively. In rats with mild fibrosis (F0-F1), severe steatosis had a significant effect on the mean SWV values. In rats with significant fibrosis (F2-F4), severe lobular inflammation had significant effects on the mean SWV values. Our findings indicate that ARFI elastography is a promising method for differentiating non-NASH rats from NASH rats and for staging hepatic fibrosis in NASH. The presence of severe steatosis and severe lobular inflammation are significant factors for evaluating fibrosis stages.

Noninvasive assessment of age-related stiffness of crystalline lenses in a rabbit model using ultrasound elastography.

BACKGROUND: The pathological or physiological changes of a crystalline lens directly affect the eye accommodation and transmittance, and then they increase the risk of presbyopia and cataracts for people in the middle and old age groups. There is no universally accepted quantitative method to measure the lens' mechanical properties in vivo so far. This study aims to investigate the possibility of assessing the age-related stiffness change of crystalline lens by acoustic-radiation-force-based ultrasound elastography (ARF-USE) in a rabbit model in vivo. METHODS: There were 13 New Zealand white rabbits that were divided into four groups and fed normally until they were 60 (n = 4), 90 (n = 2), 120 (n = 4), and 150 (n = 3) days old, respectively. An ARF-USE platform was built based on the Verasonics™ Vantage 256 system. The shear waves were excited and traced in the lens by a linear ultrasound probe after a rabbit was anaesthetized. RESULTS: The average group velocities were 1.38 ± 0.2 m/s, 2.06 ± 0.3 m/s, 2.07 ± 0.29 m/s, and 2.30 ± 0.28 m/s, respectively, for the four groups of rabbits. The results shows that the group velocity has a strong correlation with the day age (r = 0.84, p < 1 × 10-7) and the weight (r = 0.83, p < 1×10-7) of the rabbits while the maximum displacement has no correlations with the day age (r = 0.27, p > 0.1) and the weight (r = 0.32, p > 0.1). CONCLUSION: This study demonstrated that the group velocity measured by ARF-USE had a strong correlation with age-related stiffness in a rabbit model, suggesting that group velocity is a good biomarker to characterize the stiffness of a crystalline lens. This study also demonstrated the feasibility of using this USE technique to assess the mechanical properties of the lens in vivo for clinical or research purposes.

FUIQA: Fetal Ultrasound Image Quality Assessment With Deep Convolutional Networks.

The quality of ultrasound (US) images for the obstetric examination is crucial for accurate biometric measurement. However, manual quality control is a labor intensive process and often impractical in a clinical setting. To improve the efficiency of examination and alleviate the measurement error caused by improper US scanning operation and slice selection, a computerized fetal US image quality assessment (FUIQA) scheme is proposed to assist the implementation of US image quality control in the clinical obstetric examination. The proposed FUIQA is realized with two deep convolutional neural network models, which are denoted as L-CNN and C-CNN, respectively. The L-CNN aims to find the region of interest (ROI) of the fetal abdominal region in the US image. Based on the ROI found by the L-CNN, the C-CNN evaluates the image quality by assessing the goodness of depiction for the key structures of stomach bubble and umbilical vein. To further boost the performance of the L-CNN, we augment the input sources of the neural network with the local phase features along with the original US data. It will be shown that the heterogeneous input sources will help to improve the performance of the L-CNN. The performance of the proposed FUIQA is compared with the subjective image quality evaluation results from three medical doctors. With comprehensive experiments, it will be illustrated that the computerized assessment with our FUIQA scheme can be comparable to the subjective ratings from medical doctors.

[Research Progress of Medical Ultrasound Assessment of Placental Function].

Our country has been using maturity grading method, which was proposed by Grammum in 1979, to evaluate the placental function. However, this method is subjective to consequence because it totally depends on the observation and experiences of clinicians. With the development of ultrasound technology, therefore, we reviewed more novel applications in other aspects of placenta (such as blood flow, vascularization, etc). Over the past years, scholars in the world have done a lot of research around these topics. In this review we introduce placental maturity grading with B-mode ultrasound, placental vascularization qualitative and quantitative analysis with three-dimensional Doppler ultrasound and placental volume measurement, respectively.

Automation-assisted cervical cancer screening in manual liquid-based cytology with hematoxylin and eosin staining.

Current automation-assisted technologies for screening cervical cancer mainly rely on automated liquid-based cytology slides with proprietary stain. This is not a cost-efficient approach to be utilized in developing countries. In this article, we propose the first automation-assisted system to screen cervical cancer in manual liquid-based cytology (MLBC) slides with hematoxylin and eosin (H&E) stain, which is inexpensive and more applicable in developing countries. This system consists of three main modules: image acquisition, cell segmentation, and cell classification. First, an autofocusing scheme is proposed to find the global maximum of the focus curve by iteratively comparing image qualities of specific locations. On the autofocused images, the multiway graph cut (GC) is performed globally on the a* channel enhanced image to obtain cytoplasm segmentation. The nuclei, especially abnormal nuclei, are robustly segmented by using GC adaptively and locally. Two concave-based approaches are integrated to split the touching nuclei. To classify the segmented cells, features are selected and preprocessed to improve the sensitivity, and contextual and cytoplasm information are introduced to improve the specificity. Experiments on 26 consecutive image stacks demonstrated that the dynamic autofocusing accuracy was 2.06 µm. On 21 cervical cell images with nonideal imaging condition and pathology, our segmentation method achieved a 93% accuracy for cytoplasm, and a 87.3% F-measure for nuclei, both outperformed state of the art works in terms of accuracy. Additional clinical trials showed that both the sensitivity (88.1%) and the specificity (100%) of our system are satisfyingly high. These results proved the feasibility of automation-assisted cervical cancer screening in MLBC slides with H&E stain, which is highly desirable in community health centers and small hospitals.

Partner violence and sexual jealousy in China: a population-based survey.

Using data from a nationally representative survey in China, this article examines the prevalence and risk factors for partner violence with a special focus on the important role of sexual jealousy. Among women aged 20 to 49, 7.2% reported that they were hit by their partners in the past year. Comparison shows that the Chinese prevalence is modestly below the overall median for other societies. Net of other factors, jealousy exacerbates hitting for both men and women in a reactive pattern, with the jealous partner getting hit. This suggests a rethinking of the role of sexual jealousy in spousal violence in some social settings.