Medical Imaging
Objective: Stereoelectroencephalography (SEEG) is an established invasive diagnostic technique for use in patients with drug-resistant focal epilepsy evaluated before resective epilepsy surgery. The factors that influence the accuracy of electrode implantation are not fully understood. Adequate accuracy prevents the risk of major surgery complications. Precise knowledge of the anatomical positions of individual electrode contacts is crucial for the interpretation of SEEG recordings and subsequent surgery.
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Three exemplary knee bone models derived from ultrasound imaging and their respective magnetic resonance imaging reference.
Apart from the ground truth, a partial scan as accessable by ultrasound imaging as well as full bone model computed by a statistical shape model is provided.
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227 Views
We have utilized Ultrasound (US) B-mode imaging to record single agents and collective swarms of microrobots in controlled experimental conditions.
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340 Views
Ovarian cancer is among the top health issues faced by women everywhere in the world . Ovarian tumours have a wide range of possible causes. Detecting and tracking down these cancers in their early stages is difficult which adds to the difficulty of treatment. In most cases, a woman finds out she has ovarian cancer after it has already spread. In addition, as technology in the field of artificial intelligence advances, detection can be done at an earlier level. Having this data will assist the gynaecologist in treating these tumours as soon as possible.
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1845 Views
This dataset consists of both non-retinal detachment and rhegmatogenous retinal detachment fundus images. The fundus images were collected from the four eye hospital in the country (namely India) such as Silchar medical college and hospital (Assam), Aravind eye hospital (Tamil Nadu), LV prasad eye hospital (Hyderabad), and Medanta- The medicity (Gurugram). A total of 1693 images have been collected from these hospitals of which 1017 fundus images belonged to retinal detachments and the rest 676 were non-retinal detachments.
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839 Views
This is the supplemental documents which include the latex file and figure files for the manuscript titled "Accelerating Magnetic Resonance T1ρ Mapping Using Simultaneously Spatial Patch-based and Parametric Group-based Low-rank Tensors (SMART) ".
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26 Views
Fabdepth HMI is designed for hand gesture detection for Human Machine Interaction. It contains total of 8 gestures performed by 150 different individuals. These individuals range from toddlers to senior citizens which adds diversity in this dataset. These gestures are available in 3 different formats namely resized, foreground=-background separated and depth estimated images. Additional aspect is added in terms of video format of 150 samples. Researchers may choose their combination of data modalities based on their application.
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247 Views
We provide the abstract from the paper below:
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The datset inculed the BrainWeb data which consists of T1-weighted (T1w), T2-weighted (T2w), and proton density-weighted (PDw) normal brain noise-free MR images (the size is with resolution), two real T1w MR brain datasets (OAS30040 and OAS30072) from the Open Access Series of imaging Studies (OASIS) database,and the synthetic DW-MRI dataset
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Retinal Fundus Multi-disease Image Dataset (RFMiD 2.0) is an auxiliary dataset to our previously published dataset. RFMiD 2.0 is a more challenging dataset to research society to develop the computer-based disease diagnosis system. Diabetic Retinopathy, cataracts, and refractive error in the eye are leading disease which causes permanent vision loss more frequently. Therefore, developing an AI-based model to classify these diseases is useful for ophthalmologists. This dataset consists of 860 images of frequently and rarely observed 51 diseases.
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2578 Views