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LICENSE.txt

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endoscapes/LICENSE

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endoscapes/README.md

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+<div align="center">
+<a href="http://camma.u-strasbg.fr/">
+<img src="logo.png" width="30%">
+</a>
+</div>
+
+# The Endoscapes Dataset for Surgical Scene Segmentation, Object Detection, and Critical View of Safety Assessment
+
+## Overview
+<div align="center">
+<img src="endoscapes_examples.png" width="30%">
+</div>
+
+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.
+
+```shell
+$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.

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