Estimating displacement vector field via a cost volume computed in the feature space has shown great success in image registration, but it suffers excessive computation burdens.
Intrathoracic airway segmentation in computed tomography (CT) is a prerequisite for various respiratory disease analyses such as chronic obstructive pulmonary disease (COPD), asthma and lung cancer.
1 code implementation • 10 Mar 2023 • Minghui Zhang, Yangqian Wu, Hanxiao Zhang, Yulei Qin, Hao Zheng, Wen Tang, Corey Arnold, Chenhao Pei, Pengxin Yu, Yang Nan, Guang Yang, Simon Walsh, Dominic C. Marshall, Matthieu Komorowski, Puyang Wang, Dazhou Guo, Dakai Jin, Ya'nan Wu, Shuiqing Zhao, Runsheng Chang, Boyu Zhang, Xing Lv, Abdul Qayyum, Moona Mazher, Qi Su, Yonghuang Wu, Ying'ao Liu, Yufei Zhu, Jiancheng Yang, Ashkan Pakzad, Bojidar Rangelov, Raul San Jose Estepar, Carlos Cano Espinosa, Jiayuan Sun, Guang-Zhong Yang, Yun Gu
In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution.
Deep learning empowers the mainstream medical image segmentation methods.
In this work, we propose a new architectural CSS learning framework to learn a single deep segmentation model for segmenting a total of 143 whole-body organs.
In our LViT model, medical text annotation is incorporated to compensate for the quality deficiency in image data.
Ranked #1 on Medical Image Segmentation on MoNuSeg
1 code implementation • 25 Apr 2022 • Xirui Hou, Pengfei Guo, Puyang Wang, Peiying Liu, Doris D. M. Lin, Hongli Fan, Yang Li, Zhiliang Wei, Zixuan Lin, Dengrong Jiang, Jin Jin, Catherine Kelly, Jay J. Pillai, Judy Huang, Marco C. Pinho, Binu P. Thomas, Babu G. Welch, Denise C. Park, Vishal M. Patel, Argye E. Hillis, Hanzhang Lu
Deep-learning resting-state vascular imaging has the potential to become a useful tool in clinical cerebrovascular imaging.
Reconstructing magnetic resonance (MR) images from undersampled data is a challenging problem due to various artifacts introduced by the under-sampling operation.
However, the generalizability of models trained with the FL setting can still be suboptimal due to domain shift, which results from the data collected at multiple institutions with different sensors, disease types, and acquisition protocols, etc.
Data-driven automatic approaches have demonstrated their great potential in resolving various clinical diagnostic dilemmas in neuro-oncology, especially with the help of standard anatomic and advanced molecular MR images.
Data-driven automatic approaches have demonstrated their great potential in resolving various clinical diagnostic dilemmas for patients with malignant gliomas in neuro-oncology with the help of conventional and advanced molecular MR images.
We show that our method can synthesize high-quality US images for every manipulated segmentation label with qualitative and quantitative improvements over the recent state-of-the-art synthesis methods.
We evaluate our model on the fastMRI knee and brain datasets and the results show that the proposed model outperforms other methods and can recover more details.
Various imaging artifacts, low signal-to-noise ratio, and bone surfaces appearing several millimeters in thickness have hindered the success of ultrasound (US) guided computer assisted orthopedic surgery procedures.
We propose a novel approach for generating high quality visible-like images from Synthetic Aperture Radar (SAR) images using Deep Convolutional Generative Adversarial Network (GAN) architectures.