ali-sh07/endoscopes-laparoscopic-cholecystectomy

The Endoscapes Dataset for Surgical Scene Segmentation, Object Detection, and Critical View of Safety Assessment

Overview

We are excited to release Endoscapes2023, a comprehensive laparoscopic video dataset for surgical anatomy and tool segmentation, object detection, and Critical View of Safety (CVS) assessment. This repository provides an overview of the dataset contents, including an exploration of the types and format of the annotations as well as download links.

Contents

Endoscapes2023 focuses on a region of interest within laparoscopic cholecystectomy videos where CVS is relevant and well-defined: during the dissection phase and before the first clip/cut of the cystic artery or cystic duct. We organize its contents into three different sub-datasets:

1. Endoscapes-CVS201: 11090 frames from 201 videos annotated with CVS by 3 experts. These frames are evenly spaced at 5 second intervals, and the intermediate frames can be used to train for instance semi-supervised/temporal methods. There are a total of 58813 frames (1 fps) in the aforementioned region of interest.

2. Endoscapes-BBox201: 1933 frames from 201 videos annotated with bounding boxes for 5 anatomical structures/regions (Gallbladder, Cystic Duct, Cystic Artery, Cystic Plate, Hepatocystic Triangle Dissection) and a tool class (6 classes total). The 1933 frames correspond to 1 frame every 30 seconds from the aformentioned region of interest of each video. As for Endoscapes-CVS201, the unlabeled frames can be used for semi-supervised/temporal methods.

3. Endoscapes-Seg50: 493 frames from 50 videos annotated with instance and semantic segmentation masks for the 6 aforementioned classes. Endoscapes-Seg50 is a strict subset of Endoscapes-BBox201; we select ~25% of the 201 videos (50), sample 1 frame every 30s from the region of interest, and add segmentation annotations.

File Structure

We describe the file structure below. All annotations are in COCO-format, with CVS labels encoded as image-level tags. Of note, the CVS labels represent the average of the 3 annotators for each criterion. Decimal values indicate cases where there was disagreement among annotators.

$DATA_HOME
└── train # All training frames at 1 fps
    ├── 1_29375.jpg # SYNTAX: ${VIDEO_ID}_{FRAME_NUM}.jpg
    ├── ...
    ├── 120_85800.jpg
    ├── annotation_coco.json # Annotation File with Bounding Boxes for 1 frame every 30s
    ├── annotation_ds_coco.json # Annotation File with CVS labels for 1 frame every 5s
└── val # All validation frames at 1 fps
    ├── 121_14850.jpg
    ├── ...
    ├── 161_34325.jpg
    ├── annotation_coco.json
    ├── annotation_ds_coco.json
└── test # All testing frames at 1 fps
    ├── 162_5850.jpg
    ├── ...
    ├── 201_46125.jpg
    ├── annotation_coco.json
    ├── annotation_ds_coco.json
└── train_seg # Training frames for EndoscapesSeg50 at 1 fps
    ├── 4_21725.jpg
    ├── ...
    ├── 119_58000.jpg
    ├── annotation_coco.json # Annotation File with Instance Segmentation Masks for 1 frame every 30s
└── val_seg # Validation frames for EndoscapesSeg50 at 1 fps
    ├── 126_10825.jpg
    ├── ...
    ├── 159_60875.jpg
    ├── annotation_coco.json
└── test_seg # Testing frames for EndoscapesSeg50 at 1 fps
    ├── 165_22925.jpg
    ├── ...
    ├── 189_34875.jpg
    ├── annotation_coco.json
└── insseg # Instance segmentation masks SYNTAX: ${VIDEO_ID}_{FRAME_NUM}.npy/csv
    ├── 5_5900.npy # stack of instance masks, where each channel corresponds to an object instance
    ├── 5_5900.csv # label of each slice of numpy mask
    ├── ...
└── semseg # Semantic segmentation masks SYNTAX: ${VIDEO_ID}_{FRAME_NUM}.png
    ├── 5_5900.png # 480x854x3 image with each element representing the class_id of each pixel
    ├── ...
└── all_metadata.csv # CSV file with CVS annotations by each annotator
└── vid_cvs.csv # CSV file with Video-level CVS assessments
└── train_vids.txt # 120 training videos for Endoscapes-CVS201 and Endoscapes-BBox201
└── val_vids.txt # 41 validation videos for Endoscapes-CVS201 and Endoscapes-BBox201
└── test_vids.txt # 40 testing videos for Endoscapes-CVS201 and Endoscapes-BBox201
└── train_seg_vids.txt # 30 training videos for Endoscapes-Seg50
└── val_seg_vids.txt # 10 validation videos for Endoscapes-Seg50
└── test_seg_vids.txt # 10 testing videos for Endoscapes-Seg50
└── seg_label_map.txt # map from class name to id; note that background is ignored for object detection/instance segmentation; for these tasks, id 0 becomes cystic plate, ...

Acknowledgement

This work was supported by French state funds managed by the ANR within the National AI Chair program under Grant ANR-20-CHIA- 0029-01 (Chair AI4ORSafety) and within the Investments for the future program under Grants ANR-10-IDEX-0002-02 (IdEx Unistra) and ANR- 10-IAHU-02 (IHU Strasbourg). This work was granted access to the HPC resources of IDRIS under the allocation 2021-AD011011640R1 made by GENCI.