| Autor: |
O'Neil, Alison Q, Kascenas, Antanas, Henry, Joseph, Wyeth, Daniel, Shepherd, Matthew, Beveridge, Erin, Clunie, Lauren, Sansom, Carrie, Šeduikytė, Evelina, Muir, Keith, Poole, Ian |
| Rok vydání: |
2018 |
| Předmět: |
|
| Druh dokumentu: |
Working Paper |
| Popis: |
We present an efficient neural network method for locating anatomical landmarks in 3D medical CT scans, using atlas location autocontext in order to learn long-range spatial context. Location predictions are made by regression to Gaussian heatmaps, one heatmap per landmark. This system allows patchwise application of a shallow network, thus enabling multiple volumetric heatmaps to be predicted concurrently without prohibitive GPU memory requirements. Further, the system allows inter-landmark spatial relationships to be exploited using a simple overdetermined affine mapping that is robust to detection failures and occlusion or partial views. Evaluation is performed for 22 landmarks defined on a range of structures in head CT scans. Models are trained and validated on 201 scans. Over the final test set of 20 scans which was independently annotated by 2 human annotators, the neural network reaches an accuracy which matches the annotator variability, with similar human and machine patterns of variability across landmark classes. |
| Databáze: |
arXiv |
| Externí odkaz: |
|
