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Collaborative regression-based anatomical landmark detection
| Content Provider | Scilit |
|---|---|
| Author | Gao, Yaozong Shen, Dinggang |
| Copyright Year | 2015 |
| Description | Journal: Physics in Medicine & Biology Anatomical landmark detection plays an important role in medical image analysis, e.g. for registration, segmentation and quantitative analysis. Among the various existing methods for landmark detection, regression-based methods have recently attracted much attention due to their robustness and efficiency. In these methods, landmarks are localised through voting from all image voxels, which is completely different from the classification-based methods that use voxel-wise classification to detect landmarks. Despite their robustness, the accuracy of regression-based landmark detection methods is often limited due to (1) the inclusion of uninformative image voxels in the voting procedure, and (2) the lack of effective ways to incorporate inter-landmark spatial dependency into the detection step. In this paper, we propose a collaborative landmark detection framework to address these limitations. The concept of collaboration is reflected in two aspects. (1) Multi-resolution collaboration. A multi-resolution strategy is proposed to hierarchically localise landmarks by gradually excluding uninformative votes from faraway voxels. Moreover, for informative voxels near the landmark, a spherical sampling strategy is also designed at the training stage to improve their prediction accuracy. (2) Inter-landmark collaboration. A confidence-based landmark detection strategy is proposed to improve the detection accuracy of 'difficult-to-detect' landmarks by using spatial guidance from 'easy-to-detect' landmarks. To evaluate our method, we conducted experiments extensively on three datasets for detecting prostate landmarks and head & neck landmarks in computed tomography images, and also dental landmarks in cone beam computed tomography images. The results show the effectiveness of our collaborative landmark detection framework in improving landmark detection accuracy, compared to other state-of-the-art methods. |
| Related Links | http://europepmc.org/articles/pmc4833677?pdf=render https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4833677/pdf |
| Ending Page | 9401 |
| Page Count | 25 |
| Starting Page | 9377 |
| ISSN | 00319155 |
| e-ISSN | 13616560 |
| DOI | 10.1088/0031-9155/60/24/9377 |
| Journal | Physics in Medicine & Biology |
| Issue Number | 24 |
| Volume Number | 60 |
| Language | English |
| Publisher | IOP Publishing |
| Publisher Date | 2015-11-18 |
| Access Restriction | Open |
| Subject Keyword | Journal: Physics in Medicine & Biology Medical Informatics Based Landmark Detection Landmark Detection Framework Collaborative Landmark Detection Landmarks Are Localised |
| Content Type | Text |
| Resource Type | Article |
| Subject | Radiology, Nuclear Medicine and Imaging Radiological and Ultrasound Technology |
