Basque Biobank

Osakidetza Eusko Jaurlaritza

PICCOLO OCT (Optical Coherence Tomography) image collection


Figure: Example of coloured OCT images. Left column: healthy samples. Right colum: neoplastic samples


Preliminary version (v1)

This dataset consists of Optical Coherence Tomography (OCT) images acquired with a spectral domain commercial system (Thorlabs Callisto) from colon polyps and healthy tissue samples of rat models.

Samples have been acquired from a group of 60 neoplastic rats (clinically validated model Pirc F344/NTac-Apcam1137) and 30 hyperplastic rats (created with an induced colorectal hyperplastic murine model). Healthy samples have been acquired from a group of 10 control animals or non-lesion tissue of hyperplastic rats.

The dataset consists of the following:
healthy tissue (10 samples; 48 C-scans; 12820 B-scans)
hyperplastic polyps (13 samples; 53 C-scans; 13903 B-scans,)
neoplastic polyps (75 samples; 245 C-scans; 67964 B-scans)
*C-scan: 3D volume form of consecutive B-scan images


The dataset is organized in folders considering the target diagnosis as described above. Then, the samples folder name has the following naming conventions:
ORIGIN_ID: Group the specimen belongs to
ANIMAL_ID: specimen identifier within the group
SECTION: Section of the colon analysed (A-ascending, T-transverse, D-descending)
N_SAMPLES_SECTION: Number of samples analysed in the current section
LESION_ID: Identifier of lesion analysed
NUM_LESIONS: Total number of lesion found as part of the sample
SEQUENCE: Number identifying the C-scan. Lesions are usually form by various consecutive C-scan volumes.

To access these images, it is necessary to fill out this form: or to contact the Basque Biobank, which will inform you of the conditions for access.

Please cite this reference when using this collection:
Saratxaga, C.L.; Bote, J.; Ortega-Morán, J.F.; Picón, A.; Terradillos, E.; del Río, N.A.; Andraka, N.; Garrote, E.; Conde, O.M. Characterization of Optical Coherence Tomography Images for Colon Lesion Differentiation under Deep Learning. Appl. Sci. 2021, 11, 3119.

Full version (v2)
NOTE: This collection will be available when the associated publication is published.

The use of the dataset is restricted for research and educational purposes and use for commercial purposes is forbidden without prior written permission.

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