Automatic ischemic stroke lesion segmentation from computed tomography perfusion images by image synthesis and attention-based deep neural networks.
TLDR
A novel framework based on synthesized pseudo Diffusion-Weighted Imaging (DWI) from perfusion parameter maps to obtain better image quality and segmentation accuracy and has a potential for improving diagnosis and treatment of the ischemic stroke where access to real DWI scanning is limited.About:
This article is published in Medical Image Analysis.The article was published on 2020-10-01 and is currently open access. It has received 49 citations till now. The article focuses on the topics: Segmentation.read more
Citations
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Journal Article
Simulation and Synthesis in Medical Imaging
TL;DR: This editorial defines so-far ambiguous terms of simulation and synthesis in medical imaging and introduces the Special Issue on Simulation and Synthesis in Medical Imaging, which covers applications in cardiology, retinopathy, histopathology, neurosciences, and oncology.
Journal ArticleDOI
MH UNet: A Multi-Scale Hierarchical Based Architecture for Medical Image Segmentation
TL;DR: In this paper, a hierarchical block is introduced between the encoder-decoder for acquiring and merging features to extract multi-scale information in the proposed architecture, which achieves state-of-the-art performance on four challenging MICCAI datasets.
Journal ArticleDOI
SCOAT-Net: A novel network for segmenting COVID-19 lung opacification from CT images.
TL;DR: Wang et al. as mentioned in this paper proposed a spatial and channel-wise coarse-to-fine attention network (SCOAT-Net) for the segmentation of COVID-19 lung opacification from CT images.
Journal ArticleDOI
Joint Segmentation and Detection of COVID-19 via a Sequential Region Generation Network.
Jipeng Wu,Shengchuan Zhang,Xi Li,Jie Chen,Haibo Xu,Jiawen Zheng,Yue Gao,Yonghong Tian,Yongsheng Liang,Rongrong Ji +9 more
TL;DR: In this paper, the authors proposed a simple yet efficient and general-purpose network, called Sequential Region Generation Network (SRGNet), to jointly detect and segment the lesion areas of COVID-19.
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Rethinking the Dice Loss for Deep Learning Lesion Segmentation in Medical Images
TL;DR: In this article, the authors discuss the advantages and disadvantages of the Dice loss function, and group the extensions of the dice loss according to its improved purpose, and compare the performances of some extensions according to core references.
References
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Book ChapterDOI
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Proceedings ArticleDOI
Fully convolutional networks for semantic segmentation
TL;DR: The key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning.
Posted Content
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Proceedings ArticleDOI
Rethinking the Inception Architecture for Computer Vision
TL;DR: In this article, the authors explore ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization.
Posted Content
Rethinking the Inception Architecture for Computer Vision
TL;DR: This work is exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization.
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