哪有黄油
发表于 2025-3-23 11:18:28
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insular
发表于 2025-3-23 15:01:51
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
GIST
发表于 2025-3-23 22:06:52
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maculated
发表于 2025-3-24 01:00:32
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
细微的差异
发表于 2025-3-24 05:55:28
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Inflated
发表于 2025-3-24 06:45:11
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
bizarre
发表于 2025-3-24 13:49:47
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蜿蜒而流
发表于 2025-3-24 14:52:31
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
逃避责任
发表于 2025-3-24 21:57:47
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
临时抱佛脚
发表于 2025-3-25 03:10:22
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