diff --git a/Ultrasound/cuhk/hongliang_ren/tracked_eus/meta/README.md b/Ultrasound/cuhk/hongliang_ren/tracked_eus/meta/README.md new file mode 100644 index 0000000000000000000000000000000000000000..77cd5a4c461a2dad160942461f5501021aad8fab --- /dev/null +++ b/Ultrasound/cuhk/hongliang_ren/tracked_eus/meta/README.md @@ -0,0 +1,115 @@ + + +# [tracked_EUS] - README + +--- + +## 📋 At a Glance + +* Endoscopic ultrasound (EUS) scanning with 6-DoF pose tracking on in-vivo porcine models.* + +--- + +## 📖 Dataset Overview + +*This dataset consists of 34 trajectories of expert gastroenterologist performing EUS examinations on in-vivo porcine models +The data contains synchronized data streams including B-mode ultrasound frames, white-light endoscopic video, +and 6-DoF pose ground truth recorded via electromagnetic (EM) tracking, +capturing standard clinical scanning procedures across the upper and lower digestive tracts.* + +| | | +| :--- |:--------------------------------------------------------------------------------------------------------------------------------------------------| +| **Total Trajectories** | `[34]` | +| **Data Type** | `[ ] Clinical` `[ ] Ex-Vivo` `[ ] Table-Top Phantom` `[ ] Digital Simulation` `[ ] Physical Simulation` `[x] In-Vivo (Porcine Model)` | +| **License** | CC BY 4.0 | +| **Version** | `[e.g., 1.0]` | + +--- + +## 🎯 Tasks & Domain + +### Domain + +*Select the primary domain for this dataset.* + +- [ ] **Surgical Robotics** +- [ ] **Ultrasound Robotics** +- [x] **Endoscopic ultrasound** + +### Demonstrated Skills + +The dataset demonstrates expert maneuvering and scanning of specific anatomical stations: + +* **Vascular Scanning**: Inferior Vena Cava (IVC), Portal Vein (PV), Thoracic Aorta. +* **Organ Scanning**: Pancreas, Liver, Mediastinum. +* **Station Navigation**: Gastric station scanning, Gastric Cardia entry. +* **Procedure Phases**: Endoscope insertion, Transrectal EUS survey. + +--- + +## 🔬 Data Collection Details + +### Collection Method + +*How was the data collected?* + +- [ ] **Human Teleoperation** +- [ ] **Programmatic/State-Machine** +- [ ] **AI Policy / Autonomous** +- [x] **Human Operation** + +### Operator Details + +| | Description | +| :--- |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| **Operator Count** | `[1]` | +| **Operator Skill Level** | `[x] Expert (e.g., Surgeon, Sonographer)`
`[ ] Intermediate (e.g., Trained Researcher)`
`[ ] Novice (e.g., ML Researcher with minimal experience)`
`[ ] N/A` | +| **Collection Period** | From `[2025-11-1]` to `[2026-1-30]` | + +### Recovery Demonstrations + +*Does this dataset include examples of recovering from failure?* + +- [ ] **Yes** +- [x] **No** +--- + +## 🎯 Action & State Space Representation + +### State Space +- **`observation.state`**: `[tx, ty, tz, qx, qy, qz, qw, quality]` + - EM Tracker pose of the probe tip relative to the emitter base. + - Index 7 represents the tracker signal quality. + +### Action Space +- **`action`**: `[tx, ty, tz, qx, qy, qz, qw]` + - Target pose for the next timestep (Delta-time prediction target). + +### Metadata Fields +- **`observation.meta.probe_acquisition_param`**: `[freq, depth, gain, fps]` +- **`observation.meta.prob_cali_mtx`**: **Placeholder (Identity)**. + - *Note: Calibration between the Ultrasound image plane and the EM sensor is not currently provided. This field contains identity transforms for structural compliance.* + + +--- + +## ⏱️ Data Synchronization Approach + +*Temporal synchronization was achieved through a motion-based calibration involving periodic Z-axis probe movements +in a water tank. We calculated the optimal time offset by cross-correlating the images intensity waveform +with the EM sensor's data.* + +--- + +## 👥 Attribution & Contact + +| | | +| :--- |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| **Dataset Lead** | `[Erqi Wang, Rui Ji, Zhen Li, Ning Zhong, Xiuli Zuo, Hongliang Ren]` | +| **Institution** | `[Chinese University of Hong Kong, Qilu Hospital of Shandong University]` | +| **Contact Email** | `[ewang@link.cuhk.edu.hk]` | +| **Citation (BibTeX)** | 
@misc{[Tracked_EUS],
  author = {[Erqi Wang, Rui Ji, Zhen Li, Ning Zhong, Xiuli Zuo, Hongliang Ren]},
  title = {[Tracked_EUS]},
  year = {2025},
  publisher = {Open-H-Embodiment},
  note = {https://hrpp.research.virginia.edu/teams/irb-sbs/researcher-guide-irb-sbs/identifiers}
}
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2222} +{"episode_index": 19, "tasks": ["Transrectal EUS survey on porcine"], "length": 1001} +{"episode_index": 20, "tasks": ["Enter Gastric Cardia on porcine"], "length": 1101} +{"episode_index": 21, "tasks": ["Gastric station scanning on procine"], "length": 818} +{"episode_index": 22, "tasks": ["Scan portal vein on porcine"], "length": 764} +{"episode_index": 23, "tasks": ["Transrectal EUS survey on porcine"], "length": 1001} +{"episode_index": 24, "tasks": ["Transrectal EUS survey on porcine"], "length": 916} +{"episode_index": 25, "tasks": ["Scan protal vein and liver on porcine"], "length": 936} +{"episode_index": 26, "tasks": ["Scan portal vein on porcine"], "length": 706} +{"episode_index": 27, "tasks": ["Scan pancreas on porcine"], "length": 624} +{"episode_index": 28, "tasks": ["Scan inferior vena cava on porcine"], "length": 662} +{"episode_index": 29, "tasks": ["Transrectal EUS survey on porcine"], "length": 1200} +{"episode_index": 30, "tasks": ["Transrectal EUS survey on 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+ }, + "task_index": { + "dtype": "int64", + "shape": [ + 1 + ], + "names": null + } + } +} \ No newline at end of file diff --git a/Ultrasound/cuhk/hongliang_ren/tracked_eus/meta/tasks.jsonl b/Ultrasound/cuhk/hongliang_ren/tracked_eus/meta/tasks.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..9f00aa7a9292f1644f95087c05200aaea60ba6cf --- /dev/null +++ b/Ultrasound/cuhk/hongliang_ren/tracked_eus/meta/tasks.jsonl @@ -0,0 +1,11 @@ +{"task_index": 0, "task": "Scan inferior vena cava on porcine"} +{"task_index": 1, "task": "Scan protal vein and liver on porcine"} +{"task_index": 2, "task": "Scan thoracic aorta and mediastinum on porcine"} +{"task_index": 3, "task": "Scan pancreas on porcine"} +{"task_index": 4, "task": "Transrectal EUS survey on porcine"} +{"task_index": 5, "task": "Gastric station scanning on procine"} +{"task_index": 6, "task": "Scan thoracic aorta on porcine"} +{"task_index": 7, "task": "Insert endoscope on porcine"} +{"task_index": 8, "task": "Enter Gastric Cardia on porcine"} +{"task_index": 9, "task": "Scan portal vein on porcine"} +{"task_index": 10, "task": "Insert through esophagus on porcine"} diff --git a/Ultrasound/cuhk/hongliang_ren/tracked_us/meta/README.md b/Ultrasound/cuhk/hongliang_ren/tracked_us/meta/README.md new file mode 100644 index 0000000000000000000000000000000000000000..3f3a3c7a77fe6fd567ff34eefa79a147d655e962 --- /dev/null +++ b/Ultrasound/cuhk/hongliang_ren/tracked_us/meta/README.md @@ -0,0 +1,115 @@ + + +# [tracked_US] - README + +--- + +## 📋 At a Glance + +* Freehand ultrasound scanning on human forearm with 6-DoF pose tracking.* + +--- + +## 📖 Dataset Overview + +*This dataset consists of 34 trajectories of expert gastroenterologist performing EUS examinations on in-vivo porcine models +The data contains synchronized data streams including B-mode ultrasound frames, white-light endoscopic video, +and 6-DoF pose ground truth recorded via electromagnetic (EM) tracking, +capturing standard clinical scanning procedures across the upper and lower digestive tracts.* + +| | | +| :--- |:-------------------------------------------------------------------------------------------------------------------------------| +| **Total Trajectories** | `[30]` | +| **Data Type** | `[x] Clinical (Human Volunteer)` `[ ] Ex-Vivo` `[ ] Table-Top Phantom` `[ ] Digital Simulation` `[ ] Physical Simulation` | +| **License** | CC BY 4.0 | +| **Version** | `[e.g., 1.0]` | + +--- + +## 🎯 Tasks & Domain + +### Domain + +*Select the primary domain for this dataset.* + +- [ ] **Surgical Robotics** +- [ ] **Ultrasound Robotics** +- [x] **Endoscopic ultrasound** + +### Demonstrated Skills + +The dataset demonstrates systematic freehand scanning patterns used for sensor calibration and surface anatomical mapping: + +* **Linear Scanning**: Straight-line sweeps along the forearm (parallel/perpendicular). +* **Curvilinear Scanning**: C-shape trajectories manipulating probe orientation. +* **Complex Trajectories**: S-shape scanning covering larger surface areas. +* **Probe Orientation Control**: Maintaining contact while varying orientation (Parallel vs. Perpendicular to arm axis). + +--- + +## 🔬 Data Collection Details + +### Collection Method + +*How was the data collected?* + +- [ ] **Human Teleoperation** +- [ ] **Programmatic/State-Machine** +- [ ] **AI Policy / Autonomous** +- [x] **Human Operation** + +### Operator Details + +| | Description | +| :--- |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| **Operator Count** | `[1]` | +| **Operator Skill Level** | `[x] Expert (e.g., Surgeon, Sonographer)`
`[ ] Intermediate (e.g., Trained Researcher)`
`[ ] Novice (e.g., ML Researcher with minimal experience)`
`[ ] N/A` | +| **Collection Period** | From `[2025-11-1]` to `[2026-1-30]` | + +### Recovery Demonstrations + +*Does this dataset include examples of recovering from failure?* + +- [ ] **Yes** +- [x] **No** +--- + +## 🎯 Action & State Space Representation + +### State Space +- **`observation.state`**: `[tx, ty, tz, qx, qy, qz, qw, quality]` + - EM Tracker pose of the probe tip relative to the emitter base. + - Index 7 represents the tracker signal quality. + +### Action Space +- **`action`**: `[tx, ty, tz, qx, qy, qz, qw]` + - Target pose for the next timestep (Delta-time prediction target). + +### Metadata Fields +- **`observation.meta.probe_acquisition_param`**: `[freq, depth, gain, fps]` +- **`observation.meta.prob_cali_mtx`**: **Placeholder (Identity)**. + - *Note: Calibration between the Ultrasound image plane and the EM sensor is not currently provided. This field contains identity transforms for structural compliance.* + + +--- + +## ⏱️ Data Synchronization Approach + +*Temporal synchronization was achieved through a motion-based calibration involving periodic Z-axis probe movements +in a water tank. We calculated the optimal time offset by cross-correlating the images intensity waveform +with the EM sensor's data.* + +--- + +## 👥 Attribution & Contact + +| | | +| :--- |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| **Dataset Lead** | `[Erqi Wang, Rui Ji, Zhen Li, Ning Zhong, Xiuli Zuo, Hongliang Ren]` | +| **Institution** | `[Chinese University of Hong Kong, Qilu Hospital of Shandong University]` | +| **Contact Email** | `[ewang@link.cuhk.edu.hk]` | +| **Citation (BibTeX)** | 
@misc{[Tracked_US],
  author = {[Erqi Wang, Rui Ji, Zhen Li, Ning Zhong, Xiuli Zuo, Hongliang Ren]},
  title = {[Tracked_US]},
  year = {2025},
  publisher = {Open-H-Embodiment},
  note = {https://hrpp.research.virginia.edu/teams/irb-sbs/researcher-guide-irb-sbs/identifiers}
}
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b/Ultrasound/cuhk/xiangyu_chu/bmt_tumor_grape_insertion_dataset/bmt_tumor_grape_insertion_dataset/meta/README.md @@ -0,0 +1,234 @@ + + +# [Dataset Name] - README + +--- + +## 📋 At a Glance + +Static dual-arm in-plane needle insertion with interferences + +--- + +## 📖 Dataset Overview + +*This dataset contains 2000 trajectories of a trained researcher using the dual-arm UR5e to perform needle insertion tasks on ex-vivo tissues include objects: mimicked vessel, different simulated tumors (grape, blueberry and fish ball), pork lean, bone ,liver, kidney.* + + +| | | +| :--- | :--- | +| **Total Trajectories** | `[2000]` | +| **Total Hours** | `[25]` | +| **Data Type** | `[ ] Clinical` `[x] Ex-Vivo` `[ ] Table-Top Phantom` `[ ] Digital Simulation` `[ ] Physical Simulation` `[ ] Other (If checked, update "Other")` | +| **License** | CC BY 4.0 | +| **Version** | `[1.0]` | + +--- + +## 🎯 Tasks & Domain + +### Domain + +*Select the primary domain for this dataset.* + +- [ ] **Surgical Robotics** +- [x] **Ultrasound Robotics** +- [ ] **Other Healthcare Robotics** (Please specify: `[Your Domain]`) + +### Demonstrated Skills + +*Autonomous dual-arm robotic needle insertion.* + + + +--- + +## 🔬 Data Collection Details + +### Collection Method + +*How was the data collected?* + +- [ ] **Human Teleoperation** +- [x] **Programmatic/State-Machine** +- [ ] **AI Policy / Autonomous** +- [ ] **Other** (Please specify: `[Your Method]`) + +### Operator Details + +| | Description | +| :--- | :--- | +| **Operator Count** | `[1]` | +| **Operator Skill Level** | `[ ] Expert (e.g., Surgeon, Sonographer)`
`[x] Intermediate (e.g., Trained Researcher)`
`[ ] Novice (e.g., ML Researcher with minimal experience)`
`[ ] N/A` | +| **Collection Period** | From `[2025-12-26]` to `[2026-01-07]` | + +### Recovery Demonstrations + +*Does this dataset include examples of recovering from failure?* + +- [ ] **Yes** +- [x] **No** + + + +--- + +## 💡 Diversity Dimensions + +*Check all dimensions that were intentionally varied during data collection.* + +- [ ] **Camera Position / Angle** +- [ ] **Lighting Conditions** +- [x] **Target Object** (e.g., different phantom models, suture types) +- [x] **Spatial Layout** (e.g., placing the target suture needle in various locations) +- [ ] **Robot Embodiment** (if multiple robots were used) +- [ ] **Task Execution** (e.g., different techniques for the same task) +- [ ] **Background / Scene** +- [ ] **Other** (Please specify: `[Your Dimension]`) + +*If you checked any of the above please briefly elaborate below.* + +After each demonstration, we adjust the initial position of at least one of the robotic arm, ultrasonic probe, and target object (phantom). + +--- + +## 🛠️ Equipment & Setup + +### Robotic Platform(s) + +*List the primary robot(s) used.* + +- **Robot 1:** `[UR5e]` +- **Robot 2:** `[UR5e]` + +### Sensors & Cameras + +*List the sensors and cameras used. Specify model names where possible. (Add and remove rows as needed)* + +| Type | Model/Details | +| :--- | :--- | +| **Room/3rd Person Camera** | `[Intel RealSense D435i @ 30fps]` | +| **Medical Imager** | `[Siemens ACUSON Juniper Ultrasound System with 12L3 Transducer, B-Mode]` | + + +--- + +## 🎯 Action & State Space Representation + +*Describe how actions and robot states are represented in your dataset. This is crucial for understanding data compatibility and enabling effective policy learning.* + +### Action Space Representation + +**Primary Action Representation:** +- [x] **Absolute Cartesian** (position/orientation relative to robot base) +- [ ] **Relative Cartesian** (delta position/orientation from current pose) +- [ ] **Joint Space** (direct joint angle commands) +- [ ] **Other** (Please specify: `[Your Representation]`) + +**Orientation Representation:** +- [x] **Quaternions** (x, y, z, w) +- [ ] **Euler Angles** (roll, pitch, yaw) +- [ ] **Axis-Angle** (rotation vector) +- [ ] **Rotation Matrix** (3x3 matrix) +- [ ] **Other** (Please specify: `[Your Representation]`) + +**Reference Frame:** +- [x] **Robot Base Frame** +- [ ] **Tool/End-Effector Frame** +- [ ] **World/Global Frame** +- [ ] **Camera Frame** +- [ ] **Other** (Please specify: `[Your Frame]`) + + + + +``` +action: ["left_x", "left_y", "left_z", "left_qx", "left_qy", "left_qz", "left_qw", "right_x", "right_y", "right_z", "right_qx", "right_qy", "right_qz", "right_qw"] +- left_x, left_y, left_z, right_x, right_y, right_z: Absolute position in left and right robot base frame (meters) +- left_qx, left_qy, left_qz, left_qw, right_qx, "right_qy, right_qz, right_qw: Absolute orientation as quaternion +action.left: [x, y, z, qx, qy, qz, qw] +- x, y, z: Absolute position in left robot base frame (meters) +- qx, qy, qz, qw: Absolute orientation as quaternion +action.right: [x, y, z, qx, qy, qz, qw] +- x, y, z: Absolute position in right robot base frame (meters) +- qx, qy, qz, qw: Absolute orientation as quaternion +``` + + +### State Space Representation + +**State Information Included:** +- [x] **Joint Positions** (all articulated joints) +- [ ] **Joint Velocities** +- [ ] **End-Effector Pose** (Cartesian position/orientation) +- [ ] **Force/Torque Readings** +- [ ] **Gripper State** (position, force, etc.) +- [ ] **Other** (Please specify: `[Your State Info]`) + + + +**Example:** +``` +observation.state: [j1, j2, j3, j4, j5, j6, j7, j8, j9, j10, j11, j12] +- 'L_Base', 'L_Shoulder', 'L_Elbow', 'L_Wrist1', 'L_Wrist2' 'L_Wrist3' 'R_Base', 'R_Shoulder', 'R_Elbow', 'R_Wrist1', 'R_Wrist2', 'R_Wrist3' : Absolute joint positions for two 6-DOF arms (radians) +observation.state.left: [j1, j2, j3, j4, j5, j6] +- 'L_Base', 'L_Shoulder', 'L_Elbow', 'L_Wrist1', 'L_Wrist2' 'L_Wrist3' : Absolute joint positions for 6-DOF arm (radians) +observation.state.right: [j1, j2, j3, j4, j5, j6] +- 'R_Base', 'R_Shoulder', 'R_Elbow', 'R_Wrist1', 'R_Wrist2', 'R_Wrist3' : Absolute joint positions for 6-DOF arm (radians) +``` + +### 📋 Recommended Additional Representations +**Step Information Included:** +- [x] **Step Labels** + +**Example:** +``` +observation.step_label: ["label"] +- label: step labels represent the robot motion state, for each trajectory includes 3 labels and 5 motion sequences ('hold - alignment - hold - insertion - hold'). +``` + + + +--- + +## ⏱️ Data Synchronization Approach + + +*We collect joint kinematics from our UR5e, RGB-D frames from Intel RealSense D435 cameras and US images from Siemens Acuson Juniper with 12L3 transducer, all running in ROS 1 Noetic on the same workstation clocked with ROS Time. Both drivers stamp their outgoing messages’ header.stamp fields with the shared system clock, and we record /joint_states, /frame_grabber/us_img, /camera_top/color/image_raw, /right_tcp_pos, and /left_tcp_pos in a single rosbag session. During export to LeRobot, each data point’s ROS header.stamp is written verbatim into the timestamp attribute. Offline checks show inter-sensor skew stays below ±100 ms across a 2-minute capture.* + +--- + +## 👥 Attribution & Contact + +*Please provide attribution for the dataset creators and a point of contact.* + +| | | +| :--- | :--- | +| **Dataset Lead** | `[Zhongyu Chen, Yunxi Tang, Tianqi Yang, Xiangyu Chu]` | +| **Institution** | `[Bioinspired Robotics and Medical Technology (BMT) Lab, Department of Mechanical and Automation Engineering (MAE), The Chinese University of Hong Kong (CUHK) ]` | +| **Contact Email** | `[zhongyuchen001@cuhk.edu.hk, 1155245457@link.cuhk.edu.hk, tianqiyang@cuhk.edu.hk, xychu@mae.cuhk.edu.hk]` | + \ No newline at end of file diff --git a/Ultrasound/cuhk/xiangyu_chu/bmt_tumor_grape_insertion_dataset/bmt_tumor_grape_insertion_dataset/meta/episodes.jsonl b/Ultrasound/cuhk/xiangyu_chu/bmt_tumor_grape_insertion_dataset/bmt_tumor_grape_insertion_dataset/meta/episodes.jsonl new file mode 100644 index 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