PlaTiF: Tibial Plateau Fracture Dataset

This dataset was developed to address the lack of publicly available, expert-annotated radiographic data for tibial plateau fractures. It includes anteroposterior (AP) knee radiographs and coronal CT slices from real-world clinical cases collected at Shariati Hospital, affiliated with Tehran University of Medical Sciences. Each fracture is categorized according to the Schatzker classification system and includes expert-reviewed tibial bone segmentation masks. The dataset is intended to support research in AI-driven fracture detection, classification, and preoperative surgical planning, and was inspired by the growing need for open-access orthopedic imaging resources to advance clinical decision support tools. 🧠 Patient Dataset Structured for Tibial Plateau Fracture: 📋 Patient Demographics and Clinical Metadata:An accompanying Excel file titled Tibial Plateau Fracture Metadata.xlsx is included in the dataset. 📂 Dataset Path .\Patient Data_Part 1 .\Patient Data_Part 2 .\Patient Data_Part 3 .\Patient Data_Part 4 Each .mat file corresponds to a unique patient and contains all data related to their X-ray and segmentation masks. 📌 FILE NAMING CONVENTION: ▶ Format: Patient_ID_XXX.mat ▶ Example: Patient_ID_001.mat, Patient_ID_204.mat ▶ Description: XXX is a 3-digit unique patient ID derived from CT/X-ray folder structure. 📦 FILE CONTENT STRUCTURE: Each .mat file contains a variable named Patient_ID_XXX, which is a struct with the following format: 📁 Patient_ID_XXX ├── 🧾 im0 │ ├── 🖼️ OriginalImage → Original X-ray image │ ├── ⚫ BW → Binary mask of segmented tibial plateau │ ├── 🖼️ maskedImage → X-ray masked with segmentation │ └── 🏷️ label → Class label (1–7) for Schatzker fracture type ├── 🧾 im1 │ └── ... └── 📐 Coronal_CT (optional) → Associated CT image if available ✔ imX fields (im0, im1, im2, ...) represent multiple views/images for each patient. 🧠 USAGE NOTES: • This dataset is designed for AI-driven Schatzker fracture classification • Fields are consistent across all patients • Images and masks are spatially aligned • Use the label field in im0 for patient classification or dataset grouping 🧾 SUMMARY: • 🔢 Patients: One .mat per patient • 🖼️ Image views: Multiple (im0, im1, ...) • 🧩 Segmentation: Binary mask per image • 🏷️ Label Classes: 1 to 6 (Schatzker types)+7 (No fractures) • 📦 Optional CT: Coronal CT image per patient • 💾 Format: MATLAB .mat (v7 or higher)