Call for Papers

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ICIAR 2018 special session on
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Novel imaging methods for diagnosis and screening of ophthalmic diseases
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June 27-29, 2018, Pˇvoa de Varzim, Porto, Portugal
http://www.aimiconf.org/iciar18


Special Session Chairs
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K.A. Vermeer, PhD
Rotterdam Ophthalmic Institute, Rotterdam Eye Hospital, The Netherlands
k.vermeer@eyehospital.nl, http://www.roi.eyehospital.nl/person/1/koen-vermeer-phd

A.M. Mendonša, PhD
Faculty of Engineering & INESC Technology and Science, University of Porto, Portugal
amendon@fe.up.pt, https://sigarra.up.pt/feup/en/func_geral.formview?p_codigo=208752


Introduction
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The eye provides a unique opportunity for imaging: it is largely transparent to visible and near-IR light which enables imaging by a range of optical imaging modalities. Fundus photography, fluorescence angiography, fundus autofluorescence and optical coherence tomography (OCT) are some of the technologies that are routinely used in clinical practice. In addition, several newer modalities are being developed and evaluated, such as OCT-angiography, polarization-sensitive OCT, multispectral imaging, oximetry and Raman spectroscopy.

The resulting 2D, 3D or 4D images provide new information on the morphology and pathology of the cornea, lens, choroid and retina. Ophthalmologists cannot manually review these datasets, because of a lack of time, but also because it is largely unknown where the clinically relevant information is hidden in these datasets.
Image analysis and recognition can help to transform these large datasets to clinically meaningful information. On the image level, this includes the extraction of relevant features, transformation of image data and definition of quantitative image-based biomarkers. On the level of large datasets, technology may aid to identify biomarkers that are related to a disease or to describe previously unknown effects of ophthalmic diseases.

Several of the mature imaging modalities are available in affordable, easy-to-operate and robust imaging systems, making them suited for screening of common ophthalmic diseases such as diabetic retinopathy, glaucoma and age-related macular degeneration. Image analysis may further automate the process of interpreting the acquired imaging by using conventional feature-based analysis or, in the case of large data sets, modern deep-learning approaches.

This session focuses on image analysis methods and systems for detection and screening of ophthalmic diseases.
   
Topics that are relevant to this special session include (but are not limited to):
-    Image processing to improve ophthalmic images;
-    Registration of retinal images for mosaicking or longitudinal analysis;
-    Image analysis for the extraction of qualitative or quantitative image-based biomarkers in ophthalmology;
-    Segmentation of anatomical landmarks or lesions;
-    Deep-learning approaches, including CNN and other architectures, for diagnosis of ophthalmic diseases;
-    Advanced machine learning architectures for screening of diabetic retinopathy and other ophthalmic diseases;
-    Modelling and synthesis of ophthalmic images;
-    Embedding ophthalmic image-based CAD systems in a clinical workflow


Submission procedure
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Authors are invited to submit full papers showing original research contributions. The conference proceedings will be published in the Springer Lecture Notes in Computer Science series (Springer LNCS). Prospective authors should submit an electronic copy of their complete manuscript through the ICIAR 2018 submission system by January 22, 2018, selecting the special session topic in their submission. All submitted papers will be reviewed by at least two independent reviewers. For more information please go to https://www.aimiconf.org/iciar18.


Important Dates
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-    Paper submission deadline: January 22, 2018
-    Author notification: March 12, 2018
-    Camera-ready version: March 26, 2018
-    Paper registration: April 2, 2018
-    Conference: June 27-29, 2018