Name
Abstract | ||
|---|---|---|
In medical imaging, landmarks have significant clinical and scientific importance. Clinical measurements, derived from the landmarks, are used for diagnosis, therapy planning and interventional guidance in many cases. Automatic algorithms have been studied to reduce the need for manual placement of landmarks. Traditional machine learning techniques provide reasonable results; however, they have limitation of either robustness or precision given complexities and variabilities of the medical images. Recently, deep learning technologies have been emerging to tackle the problems. Among them, a deep reinforcement learning approach (DRL) has shown to successfully detect landmark locations by implicitly learning the optimized path from a starting location; however, its learning process can only include subsets of the almost infinite paths across the image context, and may lead to major failures if not trained with adequate dataset variations. Here, we propose a new landmark detection approach inspired from DRL. Instead of learning limited action paths in an image in a greedy manner, we construct a global action map across the whole image, which divides the image into four action regions (left, right, up and bottom) depending on the relative location towards the target landmark. The action map guides how to move to reach the target landmark from any location of the input image. This effectively translates the landmark detection problem into an image partition problem which enables us to leverage a deep image-to-image network to train a supervised action classifier for detection of the landmarks. We discuss the experiment results of two ultrasound datasets (cardiac and obstetric) by applying the proposed algorithm. It shows consistent improvement over traditional machine learning based and deep learning based methods. |
Year | Venue | Field |
|---|---|---|
2017 | MICCAI | Partition problem,Computer vision,Pattern recognition,Computer science,Medical imaging,Robustness (computer science),Artificial intelligence,Deep learning,Classifier (linguistics),Landmark,Therapy planning,Reinforcement learning |
DocType | Citations | PageRank |
Conference | 3 | 0.48 |
References | Authors | |
7 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhoubing Xu | 1 | 120 | 12.56 |
| Qiangui Huang | 2 | 20 | 1.80 |
| Jin Hyeong Park | 3 | 26 | 3.04 |
| Mingqing Chen | 4 | 35 | 5.51 |
| Daguang Xu | 5 | 33 | 3.63 |
| Dong Yang | 6 | 22 | 3.91 |
| David Liu | 7 | 87 | 7.58 |
| Zhou S. Kevin | 8 | 474 | 41.40 |