Current research in automated disease detection focuses on making algorithms “slimmer” reducing the need for large training datasets and accelerating recalibration for new data while achieving high accuracy. The development of slimmer models has become a hot research topic in medical imaging. In this work, we develop a two-phase model for glaucoma detection, identifying and exploiting a redundancy in fundus image data relating particularly to the geometry. We propose a novel algorithm for the cup and disc segmentation “EffUnet” with an efficient convolution block and combine this with an extended spatial generative approach for geometry modelling and classification, termed “SpaGen” We demonstrate the high accuracy achievable by EffUnet in detecting the optic disc and cup boundaries and show how our algorithm can be quickly trained with new data by recalibrating the EffUnet layer only. Our resulting glaucoma detection algorithm, “EffUnet-SpaGen”, is optimized to significantly reduce the computational burden while at the same time surpassing the current state-of-art in glaucoma detection algorithms with AUROC 0.997 and 0.969 in the benchmark online datasets ORIGA and DRISHTI, respectively. Our algorithm also allows deformed areas of the optic rim to be displayed and investigated, providing explainability, which is crucial to successful adoption and implementation in clinical settings.
|Number of pages||17|
|Journal||Journal of Imaging|
|Early online date||30 May 2021|
|Publication status||Published (in print/issue) - 30 Jun 2021|
Bibliographical noteFunding Information:
Acknowledgments: V.A.K., G.C. and S.C. are thankful for their support from Global Challenges Research Fund to Liverpool John Moores University.
The ORIGA dataset is a subset of the data from the Singapore Malay Eye Study (SiMES), collected from 2004 to 2007 by the Singapore Eye Research Institute and funded by the National Medical Research Council. All images were anonymized before release. The ORIGA dataset comprises 482 healthy and 168 glaucoma images from Malay adults aged 40–80. The 650 images with manually labelled optic masks are divided into 325 training images (including 72 glaucoma cases), called ORIGA-A, and 325 testing images (including 95 glaucoma cases), called ORIGA-B . The images were manually annotated by an ophthalmologist clicking on several locations of the image to indicate the optic disc and optic rim, then a best-fitting ellipse was calculated automatically. We refer to this segmentation as the ground truth. Four graders also graded the image, and a fifth grader was used for consensus.
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
- Generative model
- Machine learning