255 citations · 457 across the 10 of their papers we have counts for
10 papers
Improving Segmentation and Detection of Lesions in CT Scans Using Intensity Distribution Supervision
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. We first build an intensity-based lesio…
Improving Small Lesion Segmentation in CT Scans using Intensity Distribution Supervision: Application to Small Bowel Carcinoid Tumor
Seung Yeon Shin, Thomas C. Shen, Stephen A. Wank +1
Finding small lesions is very challenging due to lack of noticeable features, severe class imbalance, as well as the size itself. One approach to improve small lesion segmentation…
Graph-Based Small Bowel Path Tracking with Cylindrical Constraints
Seung Yeon Shin, Sungwon Lee, Ronald M. Summers
We present a new graph-based method for small bowel path tracking based on cylindrical constraints. A distinctive characteristic of the small bowel compared to other organs is the…
Extraction of Coronary Vessels in Fluoroscopic X-Ray Sequences Using Vessel Correspondence Optimization
Seung Yeon Shin, Soochahn Lee, Kyoung Jin Noh +2
We present a method to extract coronary vessels from fluoroscopic x-ray sequences. Given the vessel structure for the source frame, vessel correspondence candidates in the subseque…
Deep Reinforcement Learning for Small Bowel Path Tracking using Different Types of Annotations
Seung Yeon Shin, Ronald M. Summers
Small bowel path tracking is a challenging problem considering its many folds and contact along its course. For the same reason, it is very costly to achieve the ground-truth (GT)…
A Graph-theoretic Algorithm for Small Bowel Path Tracking in CT Scans
Seung Yeon Shin, Sungwon Lee, Ronald M. Summers
We present a novel graph-theoretic method for small bowel path tracking. It is formulated as finding the minimum cost path between given start and end nodes on a graph that is cons…
Unsupervised Domain Adaptation for Small Bowel Segmentation using Disentangled Representation
Seung Yeon Shin, Sungwon Lee, Ronald M. Summers
We present a novel unsupervised domain adaptation method for small bowel segmentation based on feature disentanglement. To make the domain adaptation more controllable, we disentan…
Deep Small Bowel Segmentation with Cylindrical Topological Constraints
Seung Yeon Shin, Sungwon Lee, Daniel C. Elton +2
We present a novel method for small bowel segmentation where a cylindrical topological constraint based on persistent homology is applied. To address the touching issue which could…
Deep Vessel Segmentation By Learning Graphical Connectivity
Seung Yeon Shin, Soochahn Lee, Il Dong Yun +1
We propose a novel deep-learning-based system for vessel segmentation. Existing methods using CNNs have mostly relied on local appearances learned on the regular image grid, withou…
Joint Weakly and Semi-Supervised Deep Learning for Localization and Classification of Masses in Breast Ultrasound Images
Seung Yeon Shin, Soochahn Lee, Il Dong Yun +2
We propose a framework for localization and classification of masses in breast ultrasound (BUS) images. We have experimentally found that training convolutional neural network base…