The purpose of this challenge is to provide standardization of methods for assessing and benchmarking deep learning approaches to ultrasound image formation from ultrasound channel data that will live beyond the challenge.

Last Updated On: 
Wed, 08/12/2020 - 11:35


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Hyperspectral (HS) imaging presents itself as a non-contact, non-ionizing and non-invasive technique, proven to be suitable for medical diagnosis. However, the volume of information contained in these images makes difficult providing the surgeon with information about the boundaries in real-time. To that end, High-Performance-Computing (HPC) platforms become necessary. This paper presents a comparison between the performances provided by five different HPC platforms while processing a spatial-spectral approach to classify HS images, assessing their main benefits and drawbacks.


Dataset description


1) Size of the images


- PD1C1: 1000 samples x 1000 lines x 100 bands

- PD1C2: 1000 samples x 1000 lines x 100 bands

- PD1C3: 1000 samples x 1000 lines x 100 bands


2) Image composition


- The information is stored band by band

- Within each band, the information is stored line by line

- The data type is float


3) Important information


This database only contains the dermatological images. The three brain images, obtained within the context of HELICoiD EU project, are already available in the following repository:


For downloading the brain images used in this research:

- PB1C1: Op12C1

- PB2C1: Op15C1

- PB3C1: Op20C1


Glaucoma is the leading cause of irreversible blindness in the world, and primary angle closure glaucoma (PACG) is one of the main subtypes. PACG patients have narrow chamber angle and can be diagnosed by goniscopy, which may cause discomfort and relies too much on personal experience. Anterior segment OCT is able to provide 3D scan of the anterior chamber and assist the ophthalmologists evaluate the condition of chamber angle. It’s faster and objective compare with goniscopy.


Welcome to the Retinal Fundus Glaucoma Challenge! REFUGE was organized as a half day Challenge in conjunction with the 5th MICCAI Workshop on Ophthalmic Medical Image Analysis (OMIA), a Satellite Event of the MICCAI 2018 conference in Granada, Spain. The goal of the challenge is to evaluate and compare automated algorithms for glaucoma detection and optic disc/cup segmentation on a common dataset of retinal fundus images. With this challenge, we made available a large dataset of 1200 annotated retinal fundus images.


Pathologic Myopia Challenge (PALM), as a part of the serial challenge iChallenge, is organized as a half day Challenge, a Satellite Event of the ISBI 2019 conference in Venice, Italy. The PALM challenge focuses on the investigation and development of algorithms associated with the diagnosis of Pathological Myopia (PM) and segmentation of lesions in fundus photos from PM patients. The goal of the challenge is to evaluate and compare automated algorithms for the detection of pathological myopia on a common dataset of retinal fundus images.


FRAP curve modeling using transient-sensitive analog computer unit with oscilloscopic CRT (Practicum, 2014)


RECOVERY-FA19 dataset is established for development and evaluation of retinal vessel detection algorithms in fluorescein angiography (FA). RECOVERY-FA19 provides 8 high-resolution ultra-widefield FA images acquired using Optos California P200DTx camera and corresponding labeled binary vessel maps.


Ultra-widefield fluorescein angiography images and corresponding labeled vessel maps are provided where the file names indicate the correspondence between them.

The vessel ground-truth labeling for the RECOVERY-FA19 dataset was performed using the methodology proposed in: 

L. Ding, M. H. Bawany, A. E. Kuriyan, R. S. Ramchandran, C. C. Wykoff, and G. Sharma, ``A novel deep learning pipeline for retinal vessel detection in fluorescein angiography,'' IEEE Trans. Image Proc., vol. 29, no. 1, 2020, accepted for publication, to appear.

Code for evaluating vessel segmentation and replicating results from the above paper can be found in the CodeOcean capsule referenced in the paper. Users of the dataset, should cite the above paper.


The provided EEG data were acquired from sixteen healthy young adults (age range 22 - 30 years) with no history of neurological, physical, or psychiatric illness. All the participants were naive BCI users who had not participated in any related experiments before. Informed consents were received from all participants.  The study has been approved by the Institutional Research Ethics Committee of Nazarbayev University.  



We present the first approach to automatically detect and track a needle in an endoscopic ultrasound (EUS) video. EUS is a fundamental medical procedure that is used extensively for biopsying targets in the upper gastro-intestinal tract. The approach enables various new applications for advancing EUS, including automatic EUS workflow analysis, automatic report generation, video summarization, technical skills assessment and quality indicator automation. Our approach does not require annotations of needles in videos, which is a laborious and demanding task.