6 Result(s)
within
**ngli Li
Health Informatics
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Chapter and Conference Paper
Automatic Fetal Ultrasound Standard Plane Detection Using Knowledge Transferred Recurrent Neural Networks
Accurate acquisition of fetal ultrasound (US) standard planes is one of the most crucial steps in obstetric diagnosis. The conventional way of standard plane acquisition requires a thorough knowledge of fetal ...
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Chapter and Conference Paper
Towards Automatic Semantic Segmentation in Volumetric Ultrasound
3D ultrasound is rapidly emerging as a viable imaging modality for routine prenatal examinations. However, lacking of efficient tools to decompose the volumetric data greatly limits its widespread. In this pap...
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Chapter and Conference Paper
Generalizing Deep Models for Ultrasound Image Segmentation
Deep models are subject to performance drop when encountering appearance discrepancy, even on congeneric corpus in which objects share the similar structure but only differ slightly in appearance. This perform...
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Chapter and Conference Paper
Automatic and Efficient Standard Plane Recognition in Fetal Ultrasound Images via Multi-scale Dense Networks
The determination and interpretation of fetal standard planes (FSPs) in ultrasound examinations are the precondition and essential step for prenatal ultrasonography diagnosis. However, identifying multiple sta...
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Chapter and Conference Paper
Quality Assessment of Fetal Head Ultrasound Images Based on Faster R-CNN
Clinically, the transthalamic plane of the fetal head is manually examined by sonographers to identify whether it is a standard plane. This examination routine is subjective, time-consuming and requires compre...
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Article
Open AccessAutomatic segmentation of 15 critical anatomical labels and measurements of cardiac axis and cardiothoracic ratio in fetal four chambers using nnU-NetV2
Accurate segmentation of critical anatomical structures in fetal four-chamber view images is essential for the early detection of congenital heart defects. Current prenatal screening methods rely on manual mea...
