{"url":"/dataset/enseg","name":"ENSeg","full_name":null,"description_markdown":"## ENSeg Dataset Overview\r\n\r\nThis dataset represents an enhanced subset of the [ENS dataset](https://link.springer.com/article/10.1007/s11063-022-11114-y). 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. \r\n\r\nThe original dataset consists of two classes:\r\n- **Control (C)**: Healthy animals.\r\n- **Walker-256 Tumor (WT)**: Animals with cancer induced by the Walker-256 Tumor.\r\n\r\nImage 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**.\r\n\r\n### Dataset Annotations\r\n\r\nOur 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)**. \r\n\r\nAnnotations were generated using [LabelMe](https://labelme.io/) 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.\r\n\r\n### Dataset Statistics\r\n\r\nAfter processing, the full dataset contains:  \r\n- **187 images**  \r\n- **9,709 annotated neuron cells**  \r\n\r\nThe table below summarizes the number of images and annotated neurons per animal tag.\r\n\r\n| Animal Tag | # of Images | # of Neurons |\r\n|------------|------------|--------------|\r\n| 2C         | 32         | 1590         |\r\n| 4C         | 31         | 1513         |\r\n| 5C         | 31         | 2211         |\r\n| 22WT       | 31         | 1386         |\r\n| 23WT       | 31         | 1520         |\r\n| 28WT       | 31         | 1489         |\r\n| **Total**  | **187**    | **9709**     |\r\n\r\n### Recommended Training Methodology\r\n\r\nDue 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:\r\n- In each session, images from one subject are isolated for testing.\r\n- The remaining images are used for training.\r\n\r\nAlthough **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.\r\n\r\nThis 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!\r\n\r\n## Project Citation\r\nIf you want to cite our article, the dataset, or the source codes contained in this repository, please used the citation (bibtex format):\r\n\r\n```\r\n@Article{felipe25enseg,\r\n    AUTHOR = {\r\n        Felipe, Gustavo Zanoni\r\n        and Nanni, Loris\r\n        and Garcia, Isadora Goulart\r\n        and Zanoni, Jacqueline Nelisis\r\n        and Costa, Yandre Maldonado e Gomes da},\r\n    TITLE = {ENSeg: A Novel Dataset and Method for the Segmentation of Enteric Neuron Cells on Microscopy Images},\r\n    JOURNAL = {Applied Sciences},\r\n    VOLUME = {15},\r\n    YEAR = {2025},\r\n    NUMBER = {3},\r\n    ARTICLE-NUMBER = {1046},\r\n    URL = {https://www.mdpi.com/2076-3417/15/3/1046},\r\n    ISSN = {2076-3417},\r\n    DOI = {10.3390/app15031046}\r\n}\r\n```\r\n\r\n## Additional Notes\r\nPlease check out our previous datasets if you are interest into developing projects with Enteric Nervous System images:\r\n1. [EGC-Z](https://github.com/gustavozf/EGC_Z_dataset): 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;\r\n2. [ENS](https://github.com/gustavozf/ENS_dataset): 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.\r\n\r\nFor more details, please contact the main author of this project or create an issue on the project.","description_withheld":null,"homepage":"https://github.com/gustavozf/seg-lib","introduced_date":"2025-01-21","introduced_date_note":null,"introduced_by":{"paper":"/paper/enseg-a-novel-dataset-and-method-for-the","title":"ENSeg: A Novel Dataset and Method for the Segmentation of Enteric Neuron Cells on Microscopy Images","first_author":"Gustavo Zanoni Felipe","url":null},"license":{"name":"CC BY-NC-SA 4.0","url":"https://creativecommons.org/licenses/by-nc-sa/4.0/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Biology","url":"/datasets/modality/biology"},{"name":"Medical","url":"/datasets/modality/medical"}],"tasks":[{"name":"Image Classification","url":"/task/image-classification","datasets_with_task":"/datasets/task/image-classification"},{"name":"2D Object Detection","url":"/task/2d-object-detection","datasets_with_task":"/datasets/task/2d-object-detection"},{"name":"Medical Image Segmentation","url":"/task/medical-image-segmentation","datasets_with_task":"/datasets/task/medical-image-segmentation"},{"name":"Instance Segmentation","url":"/task/instance-segmentation","datasets_with_task":"/datasets/task/instance-segmentation"},{"name":"Image Segmentation","url":"/task/image-segmentation","datasets_with_task":"/datasets/task/image-segmentation"},{"name":"Medical Image Classification","url":"/task/medical-image-classification","datasets_with_task":"/datasets/task/medical-image-classification"},{"name":"Medical Object Detection","url":"/task/medical-object-detection","datasets_with_task":"/datasets/task/medical-object-detection"}],"languages":[],"variants":["ENSeg"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/medical-image-segmentation-on-enseg","task":"Medical Image Segmentation","dataset_variant":"ENSeg","rows":1,"metrics":["mDice"],"first_row_in_archive_order":{"model":"YOLOv8-m + SAM-b","paper":"/paper/enseg-a-novel-dataset-and-method-for-the","metrics":{"mDice":"0.7877"},"code_links":[{"title":"gustavozf/seg-lib","url":"https://github.com/gustavozf/seg-lib"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/enseg-a-novel-dataset-and-method-for-the","title":"ENSeg: A Novel Dataset and Method for the Segmentation of Enteric Neuron Cells on Microscopy Images","date":"2025-01-21","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}