Xiaoli Lv

Bio: Xiaoli Lv is an academic researcher from Tongji University. The author has contributed to research in topics: Gestational age. The author has an hindex of 2, co-authored 2 publications receiving 6 citations.

A preliminary study to quantitatively evaluate the development of maturation degree for fetal lung based on transfer learning deep model from ultrasound images.

Abstract: The evaluation of fetal lung maturity is critical for clinical practice since the lung immaturity is an important cause of neonatal morbidity and mortality. For the evaluation of the development of fetal lung maturation degree, our study established a deep model from ultrasound images of four-cardiac-chamber view plane. A two-stage transfer learning approach is proposed for the purpose of the study. A specific U-net structure is designed for the applied deep model. In the first stage, the model is to first learn the recognition of fetal lung region in the ultrasound images. It is hypothesized in our study that the development of fetal lung maturation degree is generally proportional to the gestational age. Then, in the second stage, the pretrained deep model is trained to accurately estimate the gestational age from the fetal lung region of ultrasound images. Totally 332 patients were included in our study, while the first 206 patients were used for training and the subsequent 126 patients were used for the independent testing. The testing results of the established deep model have the imprecision as 1.56 ± 2.17 weeks on the gestational age estimation. Its correlation coefficient with the ground truth of gestational age achieves 0.7624 (95% CI 0.6779 to 0.8270, P value < 0.00001). The hypothesis that the development of fetal lung maturation degree can be represented by the texture information from ultrasound images has been preliminarily validated. The fetal lung maturation degree can be considered as being represented by the deep model’s output denoted by the estimated gestational age.

Association of Menstrual Extension and Surgery Effectiveness with Ultrasound Parameters of Cesarean Section Scar Diverticulum in Patients Undergoing Transvaginal Uterine Diverticulum Repair.

Abstract: The association of residual myometrium thickness (RMT) and scar defect depth (D) with menstrual abnormalities and the effectiveness of vaginal repair remain to be determined in patients with cesarean section scar diverticulum (CSD). To assess the value of ultrasound to predict vaginal repair effectiveness. This was a retrospective study of patients with CSD treated with vaginal repair between 01/2014 and 02/2016 at Shanghai First Maternity and Infant Hospital (Tongji University). Transvaginal ultrasound was performed before and 3 months after surgical repair. RMT, D, scar defect length (L), and scar defect width (W) were measured. Width (W), D, and L increased along the duration of menstrual period (P < 0.05). When the menstrual extension time was ≥15 days, RMT/D and RMT/(RMT + D) were smaller than in patients with period <15 days (P < 0.05). L was the most positively correlated ultrasonic parameter with menstrual prolongation (r = 0.492). RMT/D and RMT/(RMT + D) were negatively correlated with prolonged menstruation (r = -0.304 and -0.305, respectively). RMT/D and RMT/(RMT + D) were associated with the disappearance of CSD after vaginal repair (P < 0.05). The cutoff value of RMT/(RMT + D) was 0.496, with sensitivity of 53.0% and specificity of 61.4%. L of CSD is closely correlated with menstrual extension but has no relationship with the effectiveness of surgery. RMT/(RMT + D) is correlated with menstrual extension time ≥15 days and the effectiveness of vaginal repair.

A Review of Deep Learning on Medical Image Analysis

Abstract: Compared with common deep learning methods (e.g., convolutional neural networks), transfer learning is characterized by simplicity, efficiency and its low training cost, breaking the curse of small datasets. Medical image analysis plays an indispensable role in both scientific research and clinical diagnosis. Common medical image acquisition methods include Computer Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound (US), X-Ray, etc. Although these medical imaging methods can be applied for non-invasive qualitative and quantitative analysis of patients—compared with image datasets in other computer vision fields such like faces—medical images, especially its labeling, is still scarce and insufficient. Therefore, more and more researchers adopted transfer learning for medical image processing. In this study, after reviewing one hundred representative papers from IEEE, Elsevier, Google Scholar, Web of Science and various sources published from 2000 to 2020, a comprehensive review is presented, including (i) structure of CNN, (ii) background knowledge of transfer learning, (iii) different types of strategies performing transfer learning, (iv) application of transfer learning in various sub-fields of medical image analysis, and (v) discussion on the future prospect of transfer learning in the field of medical image analysis. Through this review paper, beginners could receive an overall and systematic knowledge of transfer learning application in medical image analysis. And policymaker of related realm will benefit from the summary of the trend of transfer learning in medical imaging field and may be encouraged to make policy positive to the future development of transfer learning in the field of medical image analysis.

Prediction for pathological and immunohistochemical characteristics of triple-negative invasive breast carcinomas: the performance comparison between quantitative and qualitative sonographic feature analysis

Abstract: Sonographic features are associated with pathological and immunohistochemical characteristics of triple-negative breast cancer (TNBC). To predict the biological property of TNBC, the performance using quantitative high-throughput sonographic feature analysis was compared with that using qualitative feature assessment. We retrospectively reviewed ultrasound images, clinical, pathological, and immunohistochemical (IHC) data of 252 female TNBC patients. All patients were subgrouped according to the histological grade, Ki67 expression level, and human epidermal growth factor receptor 2 (HER2) score. Qualitative sonographic feature assessment included shape, margin, posterior acoustic pattern, and calcification referring to the Breast Imaging Reporting and Data System (BI-RADS). Quantitative sonographic features were acquired based on the computer-aided radiomics analysis. Breast cancer masses were manually segmented from the surrounding breast tissues. For each ultrasound image, 1688 radiomics features of 7 feature classes were extracted. The principal component analysis (PCA), least absolute shrinkage and selection operator (LASSO), and support vector machine (SVM) were used to determine the high-throughput radiomics features that were highly correlated to biological properties. The performance using both quantitative and qualitative sonographic features to predict biological properties of TNBC was represented by the area under the receiver operating characteristic curve (AUC). In the qualitative assessment, regular tumor shape, no angular or spiculated margin, posterior acoustic enhancement, and no calcification were used as the independent sonographic features for TNBC. Using the combination of these four features to predict the histological grade, Ki67, HER2, axillary lymph node metastasis (ALNM), and lymphovascular invasion (LVI), the AUC was 0.673, 0.680, 0.651, 0.587, and 0.566, respectively. The number of high-throughput features that closely correlated with biological properties was 34 for histological grade (AUC 0.942), 27 for Ki67 (AUC 0.732), 25 for HER2 (AUC 0.730), 34 for ALNM (AUC 0.804), and 34 for LVI (AUC 0.795). High-throughput quantitative sonographic features are superior to traditional qualitative ultrasound features in predicting the biological behavior of TNBC. • Sonographic appearances of TNBCs showed a great variety in accordance with its biological and clinical characteristics. • Both qualitative and quantitative sonographic features of TNBCs are associated with tumor biological characteristics. • The quantitative high-throughput feature analysis is superior to two-dimensional sonographic feature assessment in predicting tumor biological property.