1 code implementation • 11 Jul 2023 • Seung Yeon Shin, Thomas C. Shen, Ronald M. Summers
We propose a method to incorporate the intensity information of a target lesion on CT scans in training segmentation and detection networks.
no code implementations • 29 Jul 2022 • Seung Yeon Shin, Thomas C. Shen, Stephen A. Wank, Ronald M. Summers
Our method can be one option for explicitly incorporating intensity distribution information of a target in network training.
no code implementations • 29 Jul 2022 • Seung Yeon Shin, SungWon Lee, Ronald M. Summers
To circumvent this, a series of cylinders that are fitted along the course of the small bowel are used to guide the tracking to more reliable directions.
no code implementations • 28 Jul 2022 • Seung Yeon Shin, Soochahn Lee, Kyoung Jin Noh, Il Dong Yun, Kyoung Mu Lee
We present a method to extract coronary vessels from fluoroscopic x-ray sequences.
no code implementations • 29 Jun 2022 • Seung Yeon Shin, Ronald M. Summers
The proposed method holds a high degree of usability in this problem by being able to utilize the scans with weak annotations, and thus by possibly reducing the required annotation cost.
no code implementations • 1 Oct 2021 • Seung Yeon Shin, SungWon Lee, Ronald M. Summers
It is formulated as finding the minimum cost path between given start and end nodes on a graph that is constructed based on the bowel wall detection.
no code implementations • 6 Jul 2021 • Seung Yeon Shin, SungWon Lee, Ronald M. Summers
We present a novel unsupervised domain adaptation method for small bowel segmentation based on feature disentanglement.
no code implementations • 16 Jul 2020 • Seung Yeon Shin, Sung-Won Lee, Daniel C. Elton, James L. Gulley, Ronald M. Summers
Since the inner cylinder is free of the touching issue, a cylindrical shape constraint applied on this augmented branch guides the network to generate a topologically correct segmentation.
1 code implementation • 6 Jun 2018 • Seung Yeon Shin, Soochahn Lee, Il Dong Yun, Kyoung Mu Lee
We propose a novel deep-learning-based system for vessel segmentation.
Ranked #1 on Retinal Vessel Segmentation on HRF
1 code implementation • 10 Oct 2017 • Seung Yeon Shin, Soochahn Lee, Il Dong Yun, Sun Mi Kim, Kyoung Mu Lee
The results trained with only 10 strongly annotated images along with weakly annotated images were comparable to results trained from 800 strongly annotated images, with the 95% confidence interval of difference -3. 00%--5. 00%, in terms of the correct localization (CorLoc) measure, which is the ratio of images with intersection over union with ground truth higher than 0. 5.