This dataset is downloaded from the official website and uploaded here.

• Official website (homepage): https://www.creatis.insa-lyon.fr/Challenge/acdc/index.html
• Official ACDC download page: https://humanheart-project.creatis.insa-lyon.fr/database/#collection/637218c173e9f0047faa00fb

CVC-ClinicDB is a database of frames extracted from colonoscopy videos. These frames contain several examples of polyps. In addition to the frames, we provide the ground truth for the polyps CVC-ClinicDB is the official database to be used in the training stages of MICCAI 2015 Sub-Challenge on Automatic Polyp Detection Challenge in Colonoscopy Videos .

DENTEX CHALLENGE

We present the Dental Enumeration and Diagnosis on Panoramic X-rays Challenge (DENTEX), organized in conjunction with the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) in 2023. The primary objective of this challenge is to develop algorithms that can accurately detect abnormal teeth with dental enumeration and associated diagnosis. This not only aids in accurate treatment planning but also helps practitioners carry out procedures with a low margin of error.

The challenge provides three types of hierarchically annotated data and additional unlabeled X-rays for optional pre-training. The annotation of the data is structured using the Fédération Dentaire Internationale (FDI) system. The first set of data is partially labeled because it only includes quadrant information. The second set of data is also partially labeled but contains additional enumeration information along with the quadrant. The third data is fully labeled because it includes all quadrant-enumeration-diagnosis information for each abnormal tooth, and all participant algorithms will be benchmarked on the third data.

DENTEX aims to provide insights into the effectiveness of AI in dental radiology analysis and its potential to improve dental practice by comparing frameworks that simultaneously point out abnormal teeth with dental enumeration and associated diagnosis on panoramic dental X-rays.

DATA

The DENTEX dataset comprises panoramic dental X-rays obtained from three different institutions using standard clinical conditions but varying equipment and imaging protocols, resulting in diverse image quality reflecting heterogeneous clinical practice. The dataset includes X-rays from patients aged 12 and above, randomly selected from the hospital's database to ensure patient privacy and confidentiality.

To enable effective use of the FDI system, the dataset is hierarchically organized into three types of data;

(a) 693 X-rays labeled for quadrant detection and quadrant classes only,

(b) 634 X-rays labeled for tooth detection with quadrant and tooth enumeration classes,

(c) 1005 X-rays fully labeled for abnormal tooth detection with quadrant, tooth enumeration, and diagnosis classes.

The diagnosis class includes four specific categories: caries, deep caries, periapical lesions, and impacted teeth. An additional 1571 unlabeled X-rays are provided for pre-training.

Data Split for Evaluation and Training

The DENTEX 2023 dataset comprises three types of data: (a) partially annotated quadrant data, (b) partially annotated quadrant-enumeration data, and (c) fully annotated quadrant-enumeration-diagnosis data. The first two types of data are intended for training and development purposes, while the third type is used for training and evaluations.

To comply with standard machine learning practices, the fully annotated third dataset, consisting of 1005 panoramic X-rays, is partitioned into training, validation, and testing subsets, comprising 705, 50, and 250 images, respectively. Ground truth labels are provided only for the training data, while the validation data is provided without associated ground truth, and the testing data is kept hidden from participants.

Annotation Protocol

The DENTEX provides three hierarchically annotated datasets that facilitate various dental detection tasks: (1) quadrant-only for quadrant detection, (2) quadrant-enumeration for tooth detection, and (3) quadrant-enumeration-diagnosis for abnormal tooth detection. Although it may seem redundant to provide a quadrant detection dataset, it is crucial for utilizing the FDI Numbering System. The FDI system is a globally-used system that assigns each quadrant of the mouth a number from 1 through 4. The top right is 1, the top left is 2, the bottom left is 3, and the bottom right is 4. Then each of the eight teeth and each molar are numbered 1 through 8. The 1 starts at the front middle tooth, and the numbers rise the farther back we go. So for example, the back tooth on the lower left side would be 48 according to FDI notation, which means quadrant 4, number 8. Therefore, the quadrant segmentation dataset can significantly simplify the dental enumeration task, even though evaluations will be made only on the fully annotated third data.

Description is from: https://zenodo.org/record/7812323#.ZDQE1uxBwUG

Grand Challenge: https://dentex.grand-challenge.org/

Cite: [1] Ibrahim Ethem Hamamci, Sezgin Er, Enis Simsar, Anjany Sekuboyina, Mustafa Gundogar, Bernd Stadlinger, Albert Mehl, Bjoern Menze., Diffusion-Based Hierarchical Multi-Label Object Detection to Analyze Panoramic Dental X-rays, 2023. Pre-print: https://arxiv.org/abs/2303.06500 [2] Hamamci, I., Er, S., Simsar, E., Yuksel, A., Gultekin, S., Ozdemir, S., Yang, K., Li, H., Pati, S., Stadlinger, B., & others (2023). DENTEX: An Abnormal Tooth Detecti...

Sunnybrook Cardiac Data

The Sunnybrook Cardiac Data (SCD), also known as the 2009 Cardiac MR Left Ventricle Segmentation Challenge data, consist of 45 cine-MRI images from a mixed of patients and pathologies: healthy, hypertrophy, heart failure with infarction and heart failure without infarction. Subset of this data set was first used in the automated myocardium segmentation challenge from short-axis MRI, held by a MICCAI workshop in 2009. The whole complete data set is now available in the CAP database with public domain license. Classification There are four pathological groups in this data set, which were classified based on (K Alfakih et al., JMRI 2003) paper, i.e.: 1. Heart failure with infarction (HF-I) group had ejection fraction (EF) < 40% and evidence of late gadolinium (Gd) enhancement. 2. Heart failure without infarction (HF) group had EF < 40% and no late Gd enhancement. 3. LV hypertrophy (HYP) group had normal EF (> 55%) and a ratio of left ventricular (LV) mass over body surface area is > 83 g/m2. 4. Healthy (N) group had EF > 55% and no hypertrophy. The following table shows group statistics written as average (stddev) :

N (n=9)

HYP (n=12)

HF (n=12)

HF-I (n=12)

End Diastolic Volume (ml)

115.69 (36.89)

114.39 (50.46)

233.67 (63.21)

244.92 (86.02)

End Systolic Volume (ml)

43.10 (14.74)

43.11 (24.50)

158.28 (56.34)

174.34 (90.64)

Ejection Fraction (%)

62.93 (3.65)

62.72 (9.22)

33.09 (13.07)

32.01 (12.27)

Left Ventricular Mass (g)

130.27 (32.69)

175.87 (85.70)

193.69 (39.01)

201.32 (45.24) Availability The Cardiac Atlas Project provides the dissemination of the Sunnybrook data by hosting them in the CAP databases. Finite element models (see supporting files section below) derived from these data are also provided. The whole complete data are available for any users, including the guest user account. License and attribution of these data set, including its derivatives, follows the Public Domain (CC0 1.0 Universal). If you are using this data in a publication, please cite the following reference: Radau P, Lu Y, Connelly K, Paul G, Dick AJ, Wright GA. “Evaluation Framework for Algorithms Segmenting Short Axis Cardiac MRI.” The MIDAS Journal – Cardiac MR Left Ventricle Segmentation Challenge, http://hdl.handle.net/10380/3070 Data contributor 1. Perry Radau – Sunnybrook Health Sciences Centre, Toronto, Canada. More information · The original 2009 LV Segmentation Challenge webpage. · Promotional poster for the 2009 LV Segmentation Challenge. · The challenge results published in the MIDAS journal.

The Sunnybrook Cardiac Dataset is a medical imaging dataset containing cardiac MRI scans from multiple patients. It is widely used for research in cardiac disease classification, segmentation, and early diagnosis. The dataset includes:

DICOM images: Raw cardiac MRI scans.

Patient metadata: Labels indicating normal or diseased conditions.

Annotations: Ground truth segmentations for heart structures.

Researchers can use this dataset to develop AI models for heart disease detection, segmentation of heart chambers, and cardiac function assessment.

Sources: This dataset originates from the Sunnybrook Health Sciences Centre and has been used in various medical imaging challenges. (https://www.cardiacatlas.org/sunnybrook-cardiac-data/)

Sunnybrook Cardiac Dataset – File & Folder Structure The Sunnybrook Cardiac Dataset consists of cardiac MRI scans organized into multiple folders based on patients and study types. Below is a typical structure:

/**

(https://www.cardiacatlas.org/sunnybrook-cardiac-data/)

The Cardiac Atlas Project provides the dissemination of the Sunnybrook Cardiac Data by hosting them in the CAP databases.

Description patient data Filetype File size
DICOM image batch 1 (9 cases) zip 433.6 MB
DICOM image batch 2 (10 cases) zip 839.2 MB DICOM image batch 3 (10 cases) zip 594.2 MB
DICOM image batch 4 (10 cases) zip 469.3 MB
DICOM image batch 5 (6 cases) zip 363.4 MB

Patient data csv 2.25 KB Left ventricular models zip 2.34 MB Manual contours zip 1.37 MB

Note that we ran our CAP de-identification process to rename patient IDs according to CAP ID format. To map between CAP IDs and the original Sunnybrook’s IDs that were used during the 2009 MICCAI challenge, you must use the patient data spreadsheet.

**

Types of Pathologies in the Dataset

The dataset primarily classifies patients into normal and diseased categories based on MRI scans. The pathologies covered include:

Normal (SC-N) – Healthy patients with no cardiac abnormalities.

Hypertrophic Cardiomyopathy (SC-HC) – Thickening of the heart muscle, reducing efficiency.

Dilated Cardiomyopathy (SC-DCM) – Heart's ability to pump blood is weakened due to enlargement.

Heart Failure (SC-HF) – Advanced heart failure cases with severe ventricular dysfunction.

Each patient has an Original ID in the dataset (scd_patientdata.csv), where the prefix SC-N, SC-HC, SC-DCM, or SC-HF indicates their pathology.