DeepDistance: A multi-task deep regression model for cell detection in inverted microscopy images |
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| Institution: | 1. Department of Computer Engineering, Bilkent University, Ankara TR-06800, Turkey;2. CanSyL,Graduate School of Informatics, Middle East Technical University, Ankara TR-06800, Turkey;3. Neuroscience Graduate Program, Bilkent University, Ankara TR-06800, Turkey;1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China;2. The Department of Radiology, The Fourth Hospital of Harbin Medical University, Harbin 150001, China;3. School of Physics and Astronomy, University of Manchester, Manchester, UK;4. The Department of Medical Imaging, Western University, London, Canada;5. The Digital Imaging Group of London, London, ON N6A 3K7, Canada;1. Division of Molecular Pathology, The Institute of Cancer Research, UK;2. Department of Computer Science, University of Warwick, UK;3. School of Life Sciences, University of Warwick, UK;4. Department of Mathematics, University of Warwick, UK;5. Department of Pathology, University Hospitals Coventry and Warwickshire, UK;6. The Alan Turing Institute, London, UK;7. Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK;1. Department of Biomedical Engineering, University of Florida, FL 32611 USA;2. Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, 32611, USA;3. School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Drive 637553 Singapore;1. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China;2. School of Nursing, The Hong Kong Polytechnic University, Hong Kong, China;1. Biomedical Computer Vision Group, BioQuant, IPMB, Heidelberg University and DKFZ, Im Neuenheimer Feld 267, Heidelberg, Germany;2. High-Content Analysis of the Cell (HiCell) and Advanced Biological Screening Facility, BioQuant, Heidelberg University, Germany;3. Division of Chromatin Networks, DKFZ and BioQuant, Heidelberg, Germany;1. Department of Advanced Information Technology, Kyushu University, Fukuoka, Japan;2. RIKEN Center for Integrative Medical Sciences, Yokohama, Japan |
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| Abstract: | This paper presents a new deep regression model, which we call DeepDistance, for cell detection in images acquired with inverted microscopy. This model considers cell detection as a task of finding most probable locations that suggest cell centers in an image. It represents this main task with a regression task of learning an inner distance metric. However, different than the previously reported regression based methods, the DeepDistance model proposes to approach its learning as a multi-task regression problem where multiple tasks are learned by using shared feature representations. To this end, it defines a secondary metric, normalized outer distance, to represent a different aspect of the problem and proposes to define its learning as complementary to the main cell detection task. In order to learn these two complementary tasks more effectively, the DeepDistance model designs a fully convolutional network (FCN) with a shared encoder path and end-to-end trains this FCN to concurrently learn the tasks in parallel. For further performance improvement on the main task, this paper also presents an extended version of the DeepDistance model that includes an auxiliary classification task and learns it in parallel to the two regression tasks by also sharing feature representations with them. DeepDistance uses the inner distances estimated by these FCNs in a detection algorithm to locate individual cells in a given image. In addition to this detection algorithm, this paper also suggests a cell segmentation algorithm that employs the estimated maps to find cell boundaries. Our experiments on three different human cell lines reveal that the proposed multi-task learning models, the DeepDistance model and its extended version, successfully identify the locations of cell as well as delineate their boundaries, even for the cell line that was not used in training, and improve the results of its counterparts. |
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