Received September 12, 2019, accepted September 24, 2019, date of publication September 30, 2019, date of current version October 11, 2019. Digital Object Identifier 10.1109/ACCESS.2019.2944692 TPU Cloud-Based Generalized U-Net for Eye Fundus Image Segmentation JAVIER CIVIT-MASOT 1 , FRANCISCO LUNA-PEREJÓN 2 , SATURNINO VICENTE-DÍAZ 2 , JOSÉ MARÍA RODRÍGUEZ CORRAL 3 , AND ANTÓN CIVIT 2 1 COBER S.L., 41012 Seville, Spain 2 School of Computer Engineering, 41012 Seville, Spain 3 School of Engineering, Avenida de la Universidad de Cádiz, 11519 Cádiz, Spain Corresponding author: Javier Civit-Masot (javi.civit@gmail.com) This work was supported in part by the NPP project through SAIT (2015–2018), and in part by the Spanish Government Grant (with support from the European Regional Development Fund) COFNET under Grant TEC2016-77785-P. ABSTRACT Medical images from different clinics are acquired with different instruments and settings. To perform segmentation on these images as a cloud-based service we need to train with multiple datasets to increase the segmentation independency from the source. We also require an efficient and fast segmen- tation network. In this work these two problems, which are essential for many practical medical imaging applications, are studied. As a segmentation network, U-Net has been selected. U-Net is a class of deep neural networks which have been shown to be effective for medical image segmentation. Many different U-Net implementations have been proposed. With the recent development of tensor processing units (TPU), the execution times of these algorithms can be drastically reduced. This makes them attractive for cloud services. In this paper, we study, using Google’s publicly available colab environment, a generalized fully configurable Keras U-Net implementation which uses Google TPU processors for training and prediction. As our application problem, we use the segmentation of Optic Disc and Cup, which can be applied to glaucoma detection. To obtain networks with a good performance, independently of the image acquisition source, we combine multiple publicly available datasets (RIM-One V3, DRISHTI and DRIONS). As a result of this study, we have developed a set of functions that allow the implementation of generalized U-Nets adapted to TPU execution and are suitable for cloud-based service implementation. INDEX TERMS Deep learning, segmentation as a service, TPU, U-Net, optic disc and cup, glaucoma. I. INTRODUCTION A. CLOUD BASED MEDICAL IMAGE SEGMENTATION Segmentation is the process of automatic or semi-automatic detection of limits within a 2D or 3D image. A well-known difficulty in the segmentation of medical images is the high variability in the data sources and capture technologies. First, anatomy shows very significant variations. In addition, many different image acquisition systems are used (X-ray, CT, MRI, PET, SPECT, endoscopy, etc.) to create biomedical images. The segmentation result can also be used to obtain additional diagnostic information. Among the possible appli- cations, we can find automatic measurement of organs, cell count or simulations based on the acquired information. The associate editor coordinating the review of this manuscript and approving it for publication was Yunjie Yang . The application of Deep Learning methods to medical image analysis has quickly grown in recent years [1] due to their success with different problems, including segmenta- tion. The effectiveness of these systems improves with the number and variety of the training set images. This suggests the development of cloud-based services that can be trained with several dataset initially and retrained with new datasets samples periodically. Reducing training times is an important requirement in this scenario, and Google TPUs are currently one of the most powerful resources available to train and carry out predictions for cloud-based segmentation. Another important aspect is that, in a cloud-based service, images will come from very different sources and, thus the networks must be trained as independently as possible from the acquisition source. Several segmentation researchers [2], [3] have used several datasets. However, they always train and test with each of these datasets independently. This methodology is not VOLUME 7, 2019 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/ 142379