Name
Title | ||
|---|---|---|
Scan: Structure Correcting Adversarial Network For Organ Segmentation In Chest X-Rays |
Abstract | ||
|---|---|---|
Chest X-ray (CXR) is one of the most commonly prescribed medical imaging procedures, often with over 2-10x more scans than other imaging modalities. These voluminous CXR scans place significant workloads on radiologists and medical practitioners. Organ segmentation is a key step towards effective computer-aided detection on CXR. In this work, we propose Structure Correcting Adversarial Network (SCAN) to segment lung fields and the heart in CXR images. SCAN incorporates a critic network to impose on the convolutional segmentation network the structural regularities inherent in human physiology. Specifically, the critic network learns the higher order structures in the masks in order to discriminate between the ground truth organ annotations from the masks synthesized by the segmentation network. Through an adversarial process, the critic network guides the segmentation network to achieve more realistic segmentation that mimics the ground truth. Extensive evaluation shows that our method produces highly accurate and realistic segmentation. Using only very limited training data available, our model reaches human-level performance without relying on any pretrained model. Our method surpasses the current state-of-the-art and generalizes well to CXR images from different patient populations and disease profiles. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-00889-5_30 | DEEP LEARNING IN MEDICAL IMAGE ANALYSIS AND MULTIMODAL LEARNING FOR CLINICAL DECISION SUPPORT, DLMIA 2018 |
Keywords | Field | DocType |
Chest X-ray, Medical image segmentation, Adversarial learning, Deep neural networks | Training set,Adversarial process,Pattern recognition,Computer science,Medical imaging,Segmentation,Imaging modalities,Human physiology,Ground truth,Artificial intelligence,Adversarial system | Conference |
Volume | ISSN | Citations |
11045 | 0302-9743 | 7 |
PageRank | References | Authors |
0.52 | 20 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Dai | 1 | 333 | 12.77 |
| Nanqing Dong | 2 | 26 | 3.53 |
| Zeya Wang | 3 | 14 | 2.35 |
| Xiaodan Liang | 4 | 37 | 9.73 |
| Hao Zhang | 5 | 276 | 13.13 |
| Eric P. Xing | 6 | 87 | 11.44 |