Background: Recurrent pregnancy loss (RPL) poses significant challenges in clinical management due to an unclear etiology in over half the cases. Traditional screening methods, including ultrasonographic evaluation of endometrial receptivity (ER), have been debated for their efficacy in identifying high-risk individuals. Despite the potential of artificial intelligence, notably deep learning (DL), to enhance medical imaging analysis, its application in ER assessment for RPL risk stratification remains underexplored.
View Article and Find Full Text PDFBackground: Recurrent pregnancy loss (RPL) frequently links to a prolonged endometrial receptivity (ER) window, leading to the implantation of non-viable embryos. Existing ER assessment methods face challenges in reliability and invasiveness. Radiomics in medical imaging offers a non-invasive solution for ER analysis, but complex, non-linear radiomic-ER relationships in RPL require advanced analysis.
View Article and Find Full Text PDFIEEE J Biomed Health Inform
January 2014
Ultrasonography has been widely used to evaluate duodenogastric reflux (DGR). But to the best of our knowledge, no automatic analysis system was developed to realize the quantitative computer-aided analysis. In this paper, we propose a system to perform the automatic detection of DGR in the ultrasonic image sequences by applying the automatic motion analysis.
View Article and Find Full Text PDFPurpose: Estimating the fluid motion in ultrasonic videos is a crucial step in the analysis of duodenogastric reflux. Severe image noise and illumination changes in the pyloric region (the region of interest) challenge the accurate estimation of gastric flow. In this paper, the authors propose an illumination-robust optical flow method based on the weighted cross-correlation.
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March 2012
Tracking pylorus in ultrasonic image sequences is an important step in the analysis of duodenogastric reflux (DGR). We propose a joint prediction and segmentation method (JPS) which combines optical flow with active contour to track pylorus. The goal of the proposed method is to improve the pyloric tracking accuracy by taking account of not only the connection information among edge points but also the spatio-temporal information among consecutive frames.
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