Sukesh Adiga V and Anurag Deshmukh working under the supervision of Prof. Jayanthi Sivaswamy presented the following papers at the IEEE International Symposium on Biomedical Imaging (ISBI-2019), Venice from 8-11 April.
Sukesh Adiga V and Prof. Jayanthi Sivaswamy – Matching the Characteristics of Fundus and Smartphone Camera Images
About the work: Fundus imaging with a Smartphone camera (SC) is a cost-effective solution for the assessment of retina. However, imaging at high magnification and low light levels, results in loss of details, uneven illumination and noise especially in the peripheral region. We address these problems by matching the characteristics of images from SC to those from a regular fundus camera (FC) with an architecture called ResCycleGAN. It is based on the CycleGAN with two significant changes: A residual connection is introduced to aid learning only the correction required; A structure similarity based loss function is used to improve the clarity of anatomical structures and pathologies. The proposed method can handle variations seen in normal and pathological images, acquired even without mydriasis, which is attractive in screening. The method produces consistently balanced results, outperforms CycleGAN both qualitatively and quantitatively, and has more pleasing results.
Anurag Deshmukh and Prof. Jayanthi Sivaswamy – Synthesis of Optical Nerve Head Region of Fundus Image
About the work: The Optic Disc (OD) and Optic Cup (OC) boundaries play a critical role in the detection of glaucoma. However, very few annotated datasets are available for both OD and OC that are required for segmentation. Recently, Convolutional Neural Networks have shown significant improvements in segmentation performance. However, the full potential of CNNs is hindered by the lack of a large amount of annotated training images.
To address this issue, we explore a method to generate synthetic images which can be used to augment the training data. Given the segmentation masks of OD, OC and vessels from arbitrarily different fundus images, the proposed method employs a combination of B-spline registration and GAN to generate high-quality images that ensure that the vessels bend at the edge of the OC in a realistic manner. In contrast, the existing GAN based methods for fundus image synthesis fail to capture the local details and vasculature in the Optic Nerve Head (ONH) region. The utility of the proposed method in training deep networks for the challenging problem of OC segmentation is explored and an improvement in the dice score from 0.85 to 0.902 is seen with the inclusion of the synthetic images in the training set.
The IEEE International Symposium on Biomedical Imaging (ISBI) is a scientific conference dedicated to mathematical, algorithmic, and computational aspects of biological and biomedical imaging, across all scales of observation. It fosters knowledge transfer among different imaging communities and contributes to an integrative approach to biomedical imaging. ISBI is a joint initiative from the IEEE Signal Processing Society (SPS) and the IEEE Engineering in Medicine and Biology Society (EMBS). ISBI-2019 had tutorials, challenges and a scientific program composed of plenary talks, invited special sessions, as well as oral and poster presentations of peer-reviewed papers.