Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020; 23rd International C Anne L. Martel,Purang Abolmaesumi,Leo Joskow

哪有黃油

insular

DeU-Net: Deformable U-Net for 3D Cardiac MRI Video Segmentationever, due to anisotropic resolution and ambiguous border (e.g., right ventricular endocardium), existing methods suffer from the degradation of accuracy and robustness in 3D cardiac MRI video segmentation. In this paper, we propose a novel . (DeU-Net) to fully exploit spatio-temporal information fro

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maculated

Joint Left Atrial Segmentation and Scar Quantification Based on a DNN with Spatial Encoding and Shapncorporates the continuous spatial information of the target by introducing a spatially encoded (SE) loss based on the distance transform map. Compared to conventional binary label based loss, the proposed SE loss can reduce noisy patches in the resulting segmentation, which is commonly seen for dee

細(xì)微的差異

Inflated

TexNet: Texture Loss Based Network for Gastric Antrum Segmentation in UltrasoundU) and anesthesia. GA in ultrasound images often show substantial differences in both shape and texture among subjects, leading to a challenging task of automated segmentation. To the best of our knowledge, no work has been published for this task. Meanwhile, dice similarity coefficient (DSC) based

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蜿蜒而流

Suggestive Annotation of Brain Tumour Images with Gradient-Guided Samplingcation tasks. As a data-driven science, the success of machine learning, in particular supervised learning, largely depends on the availability of manually annotated datasets. For medical imaging applications, such annotated datasets are not easy to acquire. It takes a substantial amount of time and

逃避責(zé)任

Pay More Attention to Discontinuity for Medical Image Segmentationrogress has been made recently. Yet, most existing segmentation methods still struggle at discontinuity positions (including region boundary and discontinuity within regions), especially when generalized to unseen datasets. In particular, discontinuity within regions and being close to the real regi

臨時(shí)抱佛腳

Learning 3D Features with 2D CNNs via Surface Projection for CT Volume SegmentationN) to learn 3D features. Existing methods hence learn 3D features by still relying on 2D CNNs while attempting to consider more 2D slices, but up until now it is difficulty for them to consider the whole volumetric data, resulting in information loss and performance degradation. In this paper, we pr