Datasets of Medical Imaging
These public databases of medical images may be useful for work
1) BrainWeb MRI link ,
2) Ultrasound RF data link ,
3) Ultrasound raw data link ,
4) Fetal ultrasound head circumfrence: paper and data ,
5A) Ultrasound data for segmentation provided by the paper below: Behboodi et al., RESECT-SEG: Open access annotations of intra-operative brain tumor ultrasound images, arXiv link, Dataset link,
5B) Ultrasound data for segmentation provided by the paper below:
Yap, Moi Hoon, Gerard Pons, Joan Marti, Sergi Ganau, Melcior Sentis, Reyer Zwiggelaar, Adrian K. Davison, and Robert Marti. "Automated breast ultrasound lesions detection using convolutional neural networks." IEEE journal of biomedical and health informatics 22, no. 4 (2018): 1218-1226.
5C) paper Francesco Marzola, Nens van Alfen, Jonne Doorduin, Kristen M. Meiburger, Deep learning segmentation of transverse musculoskeletal ultrasound images for neuromuscular disease assessment, Computers in Biology and Medicine, 2021, 104623, ISSN 0010-4825
5D) Ultrasound data for segmentation and classification provided by the paper below: Al-Dhabyani, Walid, et al. "Dataset of breast ultrasound images." Data in brief 28 (2020): 104863.
5E) Ultrasound data (RF and B-mode) for segmentation and classification provided by the paper below: Piotrzkowska-Wroblewska, Hanna, et al. "Open access database of raw ultrasonic signals acquired from malignant and benign breast lesions." Medical physics 44.11 (2017): 6105-6109.
5F) TN3k Haifan Gong et al Multi-Task Learning For Thyroid Nodule Segmentation With Thyroid Region Prior, ISBI 2021 paper
5G) Unsupervised Contrastive Learning of Image Representations from Ultrasound Videos with Hard Negative Mining, Soumen Basu et al, MICCAI 2022 Gallbladder (GB) malignancy
6) Liver ultrasound: CLUST,
7) 2D Echocardiography, cardiac ultrasound. Paper: Leclerc et al, Deep Learning for Segmentation Using an Open Large-Scale Dataset in 2D Echocardiography, IEEE TMI 2019. the CAMUS Database,
8) Pediatric ultrasound brain segmentations. 1300 2D US scans for training and 329 for testing. A total of 1629 in vivo B-mode US images were obtained from 20 different subjects (age<1 years old): the US Database , the paper,
8) 3D MRI and ultrasound: the BITE Database, the RESECT Database
And paper, the RESECT Database download , and MICCAI CuRIOUS Challenge
9) Ultrasound and CT of liver tumors. SYSU datasets.
10) 3D MRI of Brain: BRATS.
11) 3D MRI of Brain: WMH Segmentation.
12) 3D MRI of Brain: OASIS.
14) Chest CT from dir-lab .
15) Chest CT Scan with COVID-19 data Dataset, Paper
16) Chest CT from National Lung Screening Trial .
17a) Pancreas CT from TCIA Pancreas CT-82 .
17b) Kidney tumor CT, The kits19 challenge data: 300 kidney tumor cases with clinical context, CT semantic segmentations, and surgical outcomes, arXiv .
18) CT and MRI of several different organs Medical Decathlon .
19) CT, chest x-ray, digital pathology etc. Cancer Data Access System .
20) Liver CT LiTS - Liver Tumor Segmentation Challenge .
21) Abdominal CT IEEE TMI's DenseVNet Multi-organ Segmentation on Abdominal CT .
22) 50 abdomen CT scans from colorectal cancer chemotherapy from synapse, Beyond the Cranial Vault .
23) SCR database: Segmentation in Chest Radiographs, isi.uu.nl.
24) Lung X-ray for COVID-19, link, paper
25) Inbreast Digital mamography link .
26) Curated Breast Imaging Subset of DDSM link .
27) Dataset of breast ultrasound images link .
28) A Large-Scale Database and a CNN Model for Attention-Based Glaucoma Detection, IEEE TMI 2020 link .
29) Cancer imaging archive link .
30) Hyperkvasir multi-class image and video dataset for gastrointestinal (GI) endoscopy link .
31) Kidney Ultrasound Data Set Data link , Paper link .