UOLO - automatic object detection and segmentation in biomedical images

We propose UOLO, a novel framework for the simultaneous detection and segmentation of structures of interest in medical images. UOLO consists of an object segmentation module which intermediate abstract representations are processed and used as input for object detection. The resulting system is optimized simultaneously for detecting a class of objects and segmenting an optionally different class of structures. UOLO is trained on a set of bounding boxes enclosing the objects to detect, as well as pixel-wise segmentation information, when available. A new loss function is devised, taking into account whether a reference segmentation is accessible for each training image, in order to suitably backpropagate the error. We validate UOLO on the task of simultaneous optic disc (OD) detection, fovea detection, and OD segmentation from retinal images, achieving state-of-the-art performance on public datasets.Comment: Publised on DLMIA 2018. Licensed under the Creative Commons CC-BY-NC-ND 4.0 license: http://creativecommons.org/licenses/by-nc-nd/4.0

Automated fundus image quality assessment and segmentation of optic disc using convolutional neural networks

An automated fundus image analysis is used as a tool for the diagnosis of common retinal diseases. A good quality fundus image results in better diagnosis and hence discarding the degraded fundus images at the time of screening itself provides an opportunity to retake the adequate fundus photographs, which save both time and resources. In this paper, we propose a novel fundus image quality assessment (IQA) model using the convolutional neural network (CNN) based on the quality of optic disc (OD) visibility. We localize the OD by transfer learning with Inception v-3 model. Precise segmentation of OD is done using the GrabCut algorithm. Contour operations are applied to the segmented OD to approximate it to the nearest circle for finding its center and diameter. For training the model, we are using the publicly available fundus databases and a private hospital database. We have attained excellent classification accuracy for fundus IQA on DRIVE, CHASE-DB, and HRF databases. For the OD segmentation, we have experimented our method on DRINS-DB, DRISHTI-GS, and RIM-ONE v.3 databases and compared the results with existing state-of-the-art methods. Our proposed method outperforms existing methods for OD segmentation on Jaccard index and F-score metrics

Deep learning based approach for optic disc and optic cup semantic segmentation for glaucoma analysis in retinal fundus images

Optic disc and optic cup are one of the most recognized retinal landmarks, and there are numerous methods for their automatic detection. Segmented optic disc and optic cup are useful in providing the contextual information about the retinal image that can aid in the detection of other retinal features, but it is also useful in the automatic detection and monitoring of glaucoma. This paper proposes a deep learning based approach for the automatic optic disc and optic cup semantic segmentation, but also the new model for possible glaucoma detection. The proposed method was trained on DRIVE and DIARETDB1 image datasets and evaluated on MESSIDOR dataset, where it achieved the average accuracy of 97.3% of optic disc and 88.1% of optic cup. Detection rate of glaucoma diesis is 96.75