Medical Object Detection

The National Institutes of Health’s Clinical Center has made a large-scale dataset of CT images publicly available to help the scientific community improve detection accuracy of lesions. While most publicly available medical image datasets have less than a thousand lesions, this dataset, named DeepLesion, has over 32,000 annotated lesions (220GB) identified on CT images. DeepLesion, a dataset with 32,735 lesions in 32,120 CT slices from 10,594 studies of 4,427 unique patients. There are a variety of lesion types in this dataset, such as lung nodules, liver tumors, enlarged lymph nodes, and so on. It has the potential to be used in various medical image applications

The SKM-TEA dataset pairs raw quantitative knee MRI (qMRI) data, image data, and dense labels of tissues and pathology for end-to-end exploration and evaluation of the MR imaging pipeline. This 1.6TB dataset consists of raw-data measurements of ~25,000 slices (155 patients) of anonymized patient knee MRI scans, the corresponding scanner-generated DICOM images, manual segmentations of four tissues, and bounding box annotations for sixteen clinically relevant pathologies. ## Challenge Tracks DICOM Track: The DICOM benchmarking track uses scanner-generated DICOM images as the input for image segmentation and detection tasks. Raw Data Track: The Raw Data benchmarking track uses raw MRI data (i.e. k-space) as the input for image reconstruction, segmentation and detection tasks.

GRAZPEDWRI-DX is a public dataset of 20,327 pediatric wrist trauma X-ray images released by the University of Medicine of Graz. These X-ray images were collected by multiple pediatric radiologists at the Department for Pediatric Surgery of the University Hospital Graz between 2008 and 2018, involving 6,091 patients and a total of 10,643 studies. This dataset is annotated with 74,459 image labels, featuring a total of 67,771 labeled objects.

The evaluation of object detection models is usually performed by optimizing a single metric, e.g. mAP, on a fixed set of datasets, e.g. Microsoft COCO and Pascal VOC. Due to image retrieval and annotation costs, these datasets consist largely of images found on the web and do not represent many real-life domains that are being modelled in practice, e.g. satellite, microscopic and gaming, making it difficult to assert the degree of generalization learned by the model. We introduce the Roboflow-100 (RF100) consisting of 100 datasets, 7 imagery domains, 224,714 images, and 805 class labels with over 11,170 labelling hours. We derived RF100 from over 90,000 public datasets, 60 million public images that are actively being assembled and labelled by computer vision practitioners in the open on the web application Roboflow Universe. By releasing RF100, we aim to provide a semantically diverse, multi-domain benchmark of datasets to help researchers test their model's generalizability with real-life data. RF100 download and benchmark replication are available on GitHub.

ENSeg Dataset Overview This dataset represents an enhanced subset of the ENS dataset. The ENS dataset comprises image samples extracted from the enteric nervous system (ENS) of male adult Wistar rats (Rattus norvegicus, albius variety), specifically from the jejunum, the second segment of the small intestine. The original dataset consists of two classes: - Control (C): Healthy animals. - Walker-256 Tumor (WT): Animals with cancer induced by the Walker-256 Tumor. Image acquisition involved 13 different animals, with 7 belonging to the C class and 6 to the WT class. Each animal contributed 32 image samples obtained from the myenteric plexus. All images were captured using the same setup and configuration, stored in PNG format, with a spatial resolution of 1384 × 1036 pixels. The overall process of obtaining the images and performing the morphometric and quantitative analyses takes approximately 5 months. ### Dataset Annotations Our dataset version includes expert-annotated labels for 6 animals, tagged as 2C, 4C, 5C, 22WT, 23WT, and 28WT. The image labels were created by members of the same laboratory where the images originated: researchers from the Enteric Neural Plasticity Laboratory of the State University of Maringá (UEM). Annotations were generated using LabelMe and consist of polygons marking each neuron cell. To maintain labeling quality according to laboratory standards, only neuron cells with well-defined borders were included in the final label masks. The labeling process lasted 9 months (from November 2023 to July 2024) and was iteratively reviewed by the lead researcher of the lab. ### Dataset Statistics After processing, the full dataset contains: - 187 images - 9,709 annotated neuron cells The table below summarizes the number of images and annotated neurons per animal tag. | Animal Tag | # of Images | # of Neurons | |------------|------------|--------------| | 2C | 32 | 1590 | | 4C | 31 | 1513 | | 5C | 31 | 2211 | | 22WT | 31 | 1386 | | 23WT | 31 | 1520 | | 28WT | 31 | 1489 | | Total | 187 | 9709 | ### Recommended Training Methodology Due to the limited number of animal samples and the natural split of images, we recommend using a leave-one-out cross-validation (LOO-CV) method for training. This ensures more reliable results by performing six training sessions per experiment, where: - In each session, images from one subject are isolated for testing. - The remaining images are used for training. Although cross-validation is recommended, it is not mandatory. Future works may introduce new training methodologies as the number of annotated subjects increases. However, it is crucial to maintain images from the same source (animal) within the same data split to prevent biased results. Even though samples are randomly selected from the animals' original tissues, this approach enhances the credibility of the findings. This dataset provides a valuable resource for instance segmentation and biomedical image analysis, supporting research on ENS morphology and cancer effects. Contributions and feedback are welcome! ## Project Citation If you want to cite our article, the dataset, or the source codes contained in this repository, please used the citation (bibtex format): @Article{felipe25enseg, AUTHOR = { Felipe, Gustavo Zanoni and Nanni, Loris and Garcia, Isadora Goulart and Zanoni, Jacqueline Nelisis and Costa, Yandre Maldonado e Gomes da}, TITLE = {ENSeg: A Novel Dataset and Method for the Segmentation of Enteric Neuron Cells on Microscopy Images}, JOURNAL = {Applied Sciences}, VOLUME = {15}, YEAR = {2025}, NUMBER = {3}, ARTICLE-NUMBER = {1046}, URL = {https://www.mdpi.com/2076-3417/15/3/1046}, ISSN = {2076-3417}, DOI = {10.3390/app15031046} } ## Additional Notes Please check out our previous datasets if you are interest into developing projects with Enteric Nervous System images: 1. EGC-Z: three datasets of Enteric Glial cells images, composed of three different chronic degenerative diseases: Cancer, Diabetes Mellitus, and Rheumatoid Arthritis. Each dataset represent binary classification task, with the classes: control (healthy) and sick; 2. ENS: the ENS image datasets comprises 1248 images taken from thirteen rats distributed in two classes: control/healthy or sick. The images were created with three distinct contrast settings targeting different Enteric Nervous System cells: Enteric Neuron cells, Enteric Glial cells, or both. For more details, please contact the main author of this project or create an issue on the project.

MP-IDB comprises four species of Malaria parasites: Falciparum, Malariae, Ovale, Vivax. For each species, there are four distinct stages of life, described in the filenames as follows: R: indicates the presence of 1 or more Ring stage parasites T: indicates the presence of 1 or more Trophozoite stage parasites S: indicates the presence of 1 or more Schizont stage parasites G: indicates the presence of 1 or more Gametocyte stage parasites. Each original image has its own ground-truth, provided by expert pathologists, while every image has been analyzed from left to right, therefore every filename indicates the presence of the parasites following this indication.