The Bladder Vessel Segmentation (BlaVeS) dataset is the first publicly available dataset 
    for blood vessel segmentation in urinary bladder endoscopy images. It provides hand-labeled pixel-wise 
    annotations of prominent blood vessels to support bladder navigation, tumor monitoring, and biopsy site localization. 
    The dataset is designed to aid in developing deep learning models for improving orientation and navigation 
    during cystoscopic procedures.



    A total of 50 representative images were selected from four clinical cystoscopic video 
    recordings (8 minutes in total). Each image has a 512×512 pixel resolution and is stored in PNG format.


Dataset Structure

    pretraining/ (For initial model training)
    training/ (Labeled images for training segmentation models)
    training_augmented/ (Extended training set with augmented samples)

        testing/

            images/ (Input cystoscopic images)

                    input_image_1.png
                    input_image_2.png
                    ...


            groundtruth/ (Pixel-wise blood vessel masks)

                    mask_1.png
                    mask_2.png
                    ...