Titlebook: Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data a; 7th Joint Internatio Danail Stoyanov,

客观

Deep Learning-Based Detection and Segmentation for BVS Struts in IVOCT Imagesis malapposed during implantation, it may potentially increase the risks of late stent thrombosis. Therefore it is important to analyze struts malapposition during implantation. This paper presents an automatic method for BVS malapposition analysis in intravascular optical coherence tomography image

元音

Towards Automatic Measurement of Type B Aortic Dissection Parameters: Methods, Applications and Persfalse lumen. For type B aortic Dissection (TBAD), the tear can appear beyond the left subclavian artery or in the aortic arch according to Stanford classification. Quantitative and qualitative analysis of the geometrical and biomedical parameters of TBAD such as maximum transverse diameter of the th

咯咯笑

Prediction of FFR from IVUS Images Using Machine LearningVUS images and FFR measurements were collected from 1744 patients and 1447 lumen and plaque segmentation masks were generated from 1447 IVUS images using an automatic segmentation model trained on separate 70 IVUS images and minor manual corrections. Using total 114 features from the masks and gener

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神圣不可

An Efficient and Comprehensive Labeling Tool for Large-Scale Annotation of Fundus Imagestime and enable comprehensive and thorough assessment at clinics, but also economize large-scale data collection processes for the development of automatic algorithms. To realize efficient and thorough fundus assessment, we developed a new labeling tool with novel schemes - stepwise labeling and reg

seruting

Comedienne

Imperfect Segmentation Labels: How Much Do They Matter? been done to analyze the effect that label errors have on the performance of segmentation methodologies. Here we present a large-scale study of model performance in the presence of varying types and degrees of error in training data. We trained U-Net, SegNet, and FCN32 several times for liver segme

SNEER

Crowdsourcing Annotation of Surgical Instruments in Videos of Cataract Surgery with limited access to an expert surgeon. Likewise, automated surgical activity recognition can improve operating room workflow efficiency, teaching and self-review, and aid clinical decision support systems. However, current supervised learning methods to do so, rely on large training datasets. Cr

cocoon

Four-Dimensional ASL MR Angiography Phantoms with Noise Learned by Neural Stylingd datasets can be used, but adding realistic noise properties is especially challenging. This paper proposes using neural styling, a deep learning based algorithm, which can automatically learn noise patterns from real medical images and reproduce these patterns in the simulated datasets. In this wo

动脉

Feature Learning Based on Visual Similarity Triplets in Medical Image Analysis: A Case Study of Emphever, training CNNs requires annotated image data. Annotating medical images can be a time-consuming task and even expert annotations are subject to substantial inter- and intra-rater variability. Assessing visual similarity of images instead of indicating specific pathologies or estimating disease