--- title: 'CholecSeg8k: Laparoscopic Segmentation Dataset' url: https://www.emergentmind.com/topics/cholecseg8k-dataset type: topic --- # CholecSeg8k: Laparoscopic Segmentation Dataset CholecSeg8k is a multiclass pixel-wise semantic segmentation dataset derived from laparoscopic cholecystectomy video sequences. Designed specifically for enhancing computer-assisted surgery research, CholecSeg8k provides dense annotations for both anatomical structures and surgical instruments, supporting the development and benchmarking of algorithms for scene understanding, tool tracking, and anatomical identification in minimally invasive surgery. The dataset is based on a subset of the widely-studied Cholec80 video corpus and is publicly released for non-commercial research and educational use [2012.12453]. ## 1. Dataset Composition and Structure CholecSeg8k consists of 8,080 RGB laparoscopic frames sampled from 17 video records of cholecystectomy procedures, captured at 25 frames per second. Each source video contributes several temporally contiguous 80-frame clips with phases selected to focus on gallbladder dissection, omitting preoperative and postoperative periods [2012.12453]. All frames are provided at a spatial resolution of 854 × 480 pixels in lossless PNG format. For each frame, four corresponding PNGs are supplied: the raw RGB image, a color-coded segmentation mask, an RGBA annotation mask, and a single-channel watershed mask encoding class IDs. The directory structure organizes data by video and clip, e.g., `videoXX_startFrame/`, containing the respective 80-frame sequences [2012.12453]. The dataset totals approximately 3 GB on disk. ## 2. Annotation Protocol and Semantic Classes Annotations were produced via the PixelAnnotationTool by trained graduate annotators following phase-specific guidelines. Each frame was cross-checked by a second annotator, though only organ-class boundary consistency was prioritized; minor boundary disagreements were not always resolved [2012.12453]. Pixel-wise masks encode 13 semantic classes, comprising 10 anatomical entities, two surgical instrument types, and black background. The class taxonomy and interpretations are outlined below: | ID | Short Name | Definition | |----|-----------------------|-------------------------------------------------------------------------| | 0 | Black background | Pixels outside circular field-of-view (padding) | | 1 | Abdominal wall | Trocar boundaries, peritoneum | | 2 | Liver | Hepatic parenchyma | | 3 | GI tract | Stomach, small intestine, adjacent tissues | | 4 | Fat | Fatty tissue | | 5 | Grasper | Fenestrated-jaw surgical instrument | | 6 | Connective tissue | Loose or dense fibrous tissue | | 7 | Blood | Visible blood pools or stains | | 8 | Cystic duct | Biliary structure | | 9 | L-hook electrocautery | Hook-shaped surgical tool for dissection | | 10 | Gallbladder | Primary organ under removal | | 11 | Hepatic vein | Major vascular structure | | 12 | Liver ligament | Coronary, triangular, falciform, and related ligaments | Class pixel frequencies are strongly imbalanced; major classes (liver, abdominal wall, gallbladder, GI tract, fat) account for the overwhelming majority of labeled pixels, while surgical tools and vessels constitute <1% each [2012.12453]. ## 3. Data Splits, Preprocessing, and Usage No single train/validation/test split is prescribed in the initial release; users are encouraged to adopt 80/10/10 or cross-validation strategies across the 101 clips [2012.12453]. Later studies have implemented diverse split conventions by video or frame—for example, one scheme assigns 80% for training, 10% for validation, and 10% for testing, maintaining balance across instrument classes [2512.02188]. Another approach splits by surgical case, e.g., 75% of cases (≈6,060 frames) for training and 25% (≈2,020 frames) for testing, omitting a separate validation set for improved sample efficiency [2306.11052]. Standard training pipelines normalize per-channel mean and variance. Common augmentations include horizontal flip, random scaling, cropping, color jitter, and Gaussian blur, specifically tailored for domain generalization and feature augmentation tasks [2512.02188]. ## 4. Statistical Properties and Class Imbalance CholecSeg8k exhibits substantial label imbalance. For example, grasper and L-hook instruments are present in only 0.6% and 0.3% of all labelled pixels, respectively, despite being critical for algorithmic tool segmentation [2012.12453]. Within the instrument subset, the L-hook appears in 29% of frames and the grasper in 76%, varying by split [2403.10216]. The majority of annotated pixels belong to liver (≈30%) and abdominal wall (≈28%) categories, while most vascular and ductal classes remain under 1% [2012.12453]. Models may require class-reweighting or focal-loss schemes to offset this skew, although several benchmarks rely on default loss settings. ## 5. Evaluation Metrics and Baseline Results Segmentation performance is consistently evaluated using per-class Intersection over Union (IoU) and Dice coefficient (DSC), along with mean IoU (mIoU), pixel accuracy, precision, and recall. The reported formulas are: $$ \mathrm{IoU}_k = \frac{TP_k}{TP_k + FP_k + FN_k} $$ $$ \mathrm{Dice}_k = \frac{2\,TP_k}{2\,TP_k + FP_k + FN_k} $$ where $TP_k$, $FP_k$, $FN_k$ denote true positives, false positives, and false negatives for class $k$, respectively [2012.12453, 2512.02188]. Recent studies have reported state-of-the-art results on CholecSeg8k. For example, RobustSurg achieves a mean IoU of 89.32% and mean Dice of 93.64% across the 13 classes, compared to 87.67% and 92.45% for DeepLabv3+ [2512.02188]. Class-wise IoU for large organs (liver, fat, gallbladder) may exceed 92%, whereas tool and vessel classes show lower, more variable performance. In instrument segmentation tasks where class aggregation is used, inclusion of optical-flow maps as input (ARFlow-derived, t−1 and t−5 offsets, multiple representations) can substantially improve Dice and recall for underrepresented fast-moving instrument classes, without architecture modification [2403.10216]. For instance, the L-hook class Dice improves from 31.97% (baseline RGB) to 49.68% (with OF), highlighting the benefit of explicit temporal cues. ## 6. Applications, Limitations, and Extensions CholecSeg8k is extensively used for: - Real-time semantic guidance and highlight overlays during laparoscopic surgery - Development and benchmarking of tool segmentation, anatomical recognition, and video-based spatio-temporal models [2306.11052] - Training detection and instance segmentation algorithms, and as a source domain for visual domain adaptation [2512.02188] - Surgical SLAM and camera tracking using semantic landmarks Limitations include dominant class imbalance, lack of instances masks, small object ambiguity (especially for vessels and tools), and absence of ground-truth depth or 3D pose annotations [2012.12453]. Some annotations for minor classes are singly reviewed, contributing to potential boundary noise or label uncertainty. Appearance variability induced by lighting, blood, and surgical artifacts further challenges model generalization. Proposed extensions include addition of further cases, temporal labels, and richer instance annotation [2012.12453]. ## 7. Access, Licensing, and Usage Example CholecSeg8k is publicly hosted on Kaggle (https://www.kaggle.com/cshih/cholecseg8k) under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 license, supporting redistribution and derivative use for non-commercial research and education [2012.12453]. A minimal PyTorch-compatible pipeline for dataset access and batched evaluation is provided within the data release, enabling direct loading of images and watershed masks, pixel-level label mapping, and computation of per-class IoU (see code example in [2012.12453]). --- **References:** - “CholecSeg8k: A Semantic Segmentation Dataset for Laparoscopic Cholecystectomy Based on Cholec80” [2012.12453] - “RobustSurg: Tackling domain generalisation for out-of-distribution surgical scene segmentation” [2512.02188] - “Exploring Optical Flow Inclusion into nnU-Net Framework for Surgical Instrument Segmentation” [2403.10216] - “A spatio-temporal network for video semantic segmentation in surgical videos” [2306.11052] - “See in Depth: Training-Free Surgical Scene Segmentation with Monocular Depth Priors” [2512.05529] Source: https://www.emergentmind.com/topics/cholecseg8k-dataset