Random Expert Sampling for Deep Learning Segmentation of Acute Ischemic Stroke on Non-contrast CT

no code implementations • 7 Sep 2023 • Sophie Ostmeier, Brian Axelrod, Benjamin Pulli, Benjamin F. J. Verhaaren, Abdelkader Mahammedi, Yongkai Liu, Christian Federau, Greg Zaharchuk, Jeremy J. Heit

Conclusion: A model trained on random expert sampling can identify the presence and location of acute ischemic brain tissue on Non-Contrast CT similar to CT perfusion and with better consistency than experts.

Non-inferiority of Deep Learning Acute Ischemic Stroke Segmentation on Non-Contrast CT Compared to Expert Neuroradiologists

1 code implementation • 24 Nov 2022 • Sophie Ostmeier, Brian Axelrod, Benjamin F. J. Verhaaren, Soren Christensen, Abdelkader Mahammedi, Yongkai Liu, Benjamin Pulli, Li-Jia Li, Greg Zaharchuk, Jeremy J. Heit

The optimized model trained on expert A was compared to test experts B and C. We used a one-sided Wilcoxon signed-rank test to test for the non-inferiority of the model-expert compared to the inter-expert agreement.

ME-Net: Multi-Encoder Net Framework for Brain Tumor Segmentation

no code implementations • 21 Mar 2022 • Wenbo Zhang, Guang Yang, He Huang, Weiji Yang, Xiaomei Xu, Yongkai Liu, Xiaobo Lai

Moreover, the serious voxel imbalance between the brain tumor and the background as well as the different sizes and locations of the brain tumor makes the segmentation of 3D images a challenging problem.

Brain Tumor Segmentation Segmentation +1

Automatic Prostate Zonal Segmentation Using Fully Convolutional Network with Feature Pyramid Attention

no code implementations • 31 Oct 2019 • Yongkai Liu, Guang Yang, Sohrab Afshari Mirak, Melina Hosseiny, Afshin Azadikhah, Xinran Zhong, Robert E. Reiter, Yeejin Lee, Steven Raman, Kyunghyun Sung

Expert 1) were 0. 71 (PZ) and 0. 75 (TZ).

Segmentation