Structured layer surface segmentation for retina OCT using fully convolutional regression networks
•A novel formulation of deep network to output continuous, smooth and topology correct surfaces without post-processing.•End-to-end optimization of multiple structured surface segmentation.•An effective multitask deep network for retinal layer surface and lesion segmentation. [Display omitted] Optic...
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| Vydáno v: | Medical image analysis Ročník 68; s. 101856 |
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| Hlavní autoři: | , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Netherlands
Elsevier B.V
01.02.2021
Elsevier BV |
| Témata: | |
| ISSN: | 1361-8415, 1361-8423, 1361-8423 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | •A novel formulation of deep network to output continuous, smooth and topology correct surfaces without post-processing.•End-to-end optimization of multiple structured surface segmentation.•An effective multitask deep network for retinal layer surface and lesion segmentation.
[Display omitted]
Optical coherence tomography (OCT) is a noninvasive imaging modality with micrometer resolution which has been widely used for scanning the retina. Retinal layers are important biomarkers for many diseases. Accurate automated algorithms for segmenting smooth continuous layer surfaces with correct hierarchy (topology) are important for automated retinal thickness and surface shape analysis. State-of-the-art methods typically use a two step process. Firstly, a trained classifier is used to label each pixel into either background and layers or boundaries and non-boundaries. Secondly, the desired smooth surfaces with the correct topology are extracted by graph methods (e.g., graph cut). Data driven methods like deep networks have shown great ability for the pixel classification step, but to date have not been able to extract structured smooth continuous surfaces with topological constraints in the second step. In this paper, we combine these two steps into a unified deep learning framework by directly modeling the distribution of the surface positions. Smooth, continuous, and topologically correct surfaces are obtained in a single feed forward operation. The proposed method was evaluated on two publicly available data sets of healthy controls and subjects with either multiple sclerosis or diabetic macular edema, and is shown to achieve state-of-the art performance with sub-pixel accuracy. |
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| Bibliografie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Author Statment Yufan He: Conceptualization, Methodology, Software, Validation, Writing - Original Draft Aaron Carass: Conceptualization, Writing - Review & Editing Yihao Liu: Conceptualization, Writing - review & editing Bruno M. Jedynak: Formal analysis, Writing - review & editing Sharon D. Solomon: Resources, Writing - review & editing Shiv Saidha: Resources, Writing - review & editing Peter A. Calabresi: Resources, Writing - review & editing Jerry L. Prince: Conceptualization, Supervision, Project administration, Writing - review & editing |
| ISSN: | 1361-8415 1361-8423 1361-8423 |
| DOI: | 10.1016/j.media.2020.101856 |