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2 results for “Bounding Box Labels”
Cholec80-Boxes: Bounding-Box Labels for Surgical Tools in Five Cholecystectomy Videos
<p> </p> <p>The dataset is descriped in a pending publication titled "Cholec80-Boxes: Bounding-box Labeling Data for Surgical Tools in Cholecystectomy Images". The dataset was used in the following studies titled:</p> <ul> <li>"Surgical tool classification & localisation using attention and multi-feature fusion deep learning approach".</li> <li>"Laparoscopic video analysis using temporal, attention, and multi-feature fusion based-approaches".</li> <li>"Analysing attention convolutional neural network for surgical tool localisation: A feasibility study".</li> </ul> <p>The dataset consists of cholecystectomy images and bounding-box labels for surgical tools. These images were extracted from five videos of the Cholec80 dataset (Twinanda et al., 2016) at a rate of 1 Hz. The images are stored in '.png' format with a resolution of 854*480 pixels. Each video’s images are organized in a separate folder. The labeling data are stored in a CSV file, which contains the region of interest (ROI) labels for each surgical tool visible in the extracted images. Additionally, the CSV file provides information about each labeled image. Table 1 presents a content description of the 'ROI_Labels.csv' file.</p> <p><strong>Table 1:</strong> Description of 'ROI_Labels.csv' file.</p> <table> <tbody> <tr> <td><strong>Column Name</strong></td> <td><strong>Description</strong></td> <td><strong>Type</strong></td> </tr> <tr> <td><em>Surgery_num</em></td> <td>Procedure number in the Cholec80 dataset from which the image was extracted.</td> <td>Integer</td> </tr> <tr> <td><em>Dir</em></td> <td>Directory of the image folder.</td> <td>String</td> </tr> <tr> <td><em>FrameName</em></td> <td>Image name in the format '<em>Video_SS_fffff.png', </em>where <em>SS is the Surgery_num and fffff is the frame number in the video.</em></td> <td>String</td> </tr> <tr> <td><em>NumBBox_inFrame</em></td> <td>The bounding-box number in the image.</td> <td>Integer</td> </tr> <tr> <td><em>ToolName</em></td> <td>Name of the surgical tool.</td> <td>String</td> </tr> <tr> <td><em>BBox</em>_<em>X</em></td> <td>X-coordinate of the top-left corner.</td> <td>Integer</td> </tr> <tr> <td><em>BBox_Y</em></td> <td>Y-coordinate of the top-left corner.</td> <td>Integer</td> </tr> <tr> <td><em>BBox_Width</em></td> <td>Bounding box width.</td> <td>Integer</td> </tr> <tr> <td><em>BBox_Height</em></td> <td>Bounding box height.</td> <td>Integer</td> </tr> </tbody> </table> <p> </p> <p><strong>Citing This Dataset:</strong></p> <p>When using this dataset, please cite the following publications:</p> <ul> <li>Jalal, N. A., Alshirbaji, T. A., Docherty, P. D., Arabian, H., Laufer, B., Krueger-Ziolek, S., Neumuth, T. & Moeller, K. (2023). Laparoscopic video analysis using temporal, attention, and multi-feature fusion based-approaches. <em>Sensors</em>, <em>23</em>(4), 1958.<br><br></li> <li>Jalal, N. A., Alshirbaji, T. A., Docherty, P. D., Arabian, H., Neumuth, T., & Möller, K. (2023). Surgical tool classification & localisation using attention and multi-feature fusion deep learning approach. IFAC-PapersOnLine, 56(2), 5626-5631.</li> <li> <p>Abdulbaki Alshirbaji, T., Arabian, H., Jalal, N. A., Battistel, A., Docherty, P. D., Neumuth, T., & Moeller, K. Cholec80-Boxes: Bounding-box labeling data for surgical tools in cholecystectomy images. (<em>to be submitted</em>). </p> </li> <li>Twinanda, A. P., Shehata, S., Mutter, D., Marescaux, J., De Mathelin, M., & Padoy, N. (2016). Endonet: a deep architecture for recognition tasks on laparoscopic videos. <em>IEEE transactions on medical imaging</em>, <em>36</em>(1), 86-97.</li> </ul>
Pockmark Bounding Box Detection and Segmentation Labels
<p>This dataset contains 256x256 pixel jpeg images of gridded depth values as well as binary masks for pixels containing pockmarks (these jpegs are merged together with the depth image on the left and the mask on the right, making a 512x256 image). These are contained within the subdirectory 'PockmarkMaskAnnotations'.</p> <p>Additionally, this dataset contains a csv containing bounding box annotations (label, bounding box coordinates in terms of image pixels, and a unique integer for the label) of pockmarks in each image. These are contained within the subdirectory 'PockmarkBoxAnnotations'.</p> <p>Together, these annotations can be used to construct either a bounding box object detector, a bounding box and mask object detector, or a semantic segmentation model.</p> <p>These were the labels used for the experiments described in Lundine et al., 2023. See this reference to find original data sources to the collected bathymetry data.</p> <p>Lundine, M., Brothers, L., Trembanis, A., Deep learning-based pockmark detection: implications for quantitative seafloor characterization, Geomorphology, 2023, Volume 421, 108524, <a href="https://doi.org/10.1016/j.geomorph.2022.108524">https://doi.org/10.1016/j.geomorph.2022.108524</a>.</p> <p> </p>
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