Dental X-Ray Annotation Project

@amirrmusavifr

Hi, I’m an AI expert with professional experience in computer vision, with a proven track record of working on complex image processing and AI/ML model development. With skill sets: • Algorithm Development: Strong understanding of computer vision algorithms and techniques, including convolutional neural networks (CNNs), object detection, image segmentation and feature extraction. • Model Training & fine-tuning: Develop and train machine learning models tailored for image analysis and visual data interpretation. I have worked on some well-known models like YOLO, RCNN, U-Net, Deeplab, ViT etc. • AI Integration: Implement and integrate AI models into existing software and hardware systems, ensuring high performance and scalability. • Data Analysis: Analyze and process large datasets of images and video feeds to identify patterns, trends, and insights. • Data Handling: Experience in handling and processing large datasets, including image and video data. Familiarity with data augmentation techniques and synthetic data generation. • Performance Optimization: Optimize algorithms and models for real-time processing and ensure they can handle large-scale data efficiently. • Programming Skills: Proficient in programming languages such as Python, and MATLAB. Experience with deep learning frameworks like TensorFlow, PyTorch, or Keras. • Tools & Libraries: Proficiency with OpenCV, scikit-image, and other

@glennw6

Hello, I’m interested in your dental X-ray segmentation project. I have strong experience with precise pixel-level annotation and understand the importance of accurate contours and consistent class separation in medical imaging tasks. I’m comfortable using CVAT, Supervisely, Labelbox, and similar tools, with exports in COCO JSON or YOLO polygon formats. I will focus carefully on high-priority regions such as Caries, Implant, and Restoration, while also ensuring clean segmentation of Pulp and Canal structures. My workflow includes mask validation, consistency checks, and QA review to minimise annotation errors and class overlap. Deliverables will include: * Organised image/mask files by image ID * Segmentation exports in your preferred format * A brief QA report outlining validation and accuracy checks I can provide reliable, detail-oriented annotations and would be happy to complete a sample task if required. Best regards

@webnsoftindia

Hello, I can accurately annotate your dental X-rays with high-precision pixel-level segmentation, ensuring clinical-grade consistency for ML training. Scope of Work Each image will be labeled for: Background, Bone, Tooth (Enamel + Dentin), Pulp, Canal, Caries, Restoration, Filling, Implant, RCT Special attention will be given to Caries, Implant, Restoration, Pulp, and Canal for precise boundary-level accuracy. Tools CVAT / Labelbox / Supervisely (as per your workflow preference) I can start immediately after dataset access and annotation guidelines confirmation.

@Muaadh11

Hi, CV/ML engineer with multi-class medical imaging segmentation experience — CVAT, COCO JSON, YOLO polygon export, all covered. I'll annotate all 10 classes with layered masking (structural bases first, pathology overlays second) and run automated QA: overlap/gap detection, pixel coverage checks, and a written validation report. Turnaround depends on dataset size — share the image count and I'll give a firm quote. — Muaadh

@jyoshnaj2

Hello, I am interested in working on your Dental X-Ray Annotation Project. I have experience in AI/ML, Computer Vision, image segmentation, and medical image-related workflows. I can accurately perform pixel-level annotations for all required classes including Bone, Tooth, Pulp, Canal, Caries, Restoration, Filling, Implant, and RCT with strong attention to detail and consistency. I understand that precise contours for Caries, Implant, and Restoration areas are critical, and I will ensure careful labeling and clean class separation throughout the dataset. I am comfortable using annotation platforms such as CVAT, Labelbox, Supervisely, and V7 Darwin, and I can export the final annotations in COCO JSON or YOLO polygon format as required. I will provide: • Properly organized image and segmentation files • Accurate and consistent annotations • QA validation/check report • Timely communication and reliable delivery My strengths include strong Computer Vision knowledge, experience with AI dataset preparation, and focus on high-quality output suitable for model training and clinical-review-level evaluation. Estimated turnaround time: 10 days (adjustable based on dataset size). I am confident I can deliver reliable and high-quality results for this project. Looking forward to working with you. Thank you.

@Jahirul067

Hello, I’m interested in your Dental X-Ray Annotation Project. I already have experience working on medical and image annotation projects, including precise segmentation and polygon-based labeling tasks. I can provide: - Pixel-level accurate annotations - Clean class separation for Bone, Tooth, Pulp, Canal, Caries, Restoration, Implant, RCT, etc. - COCO JSON or YOLO polygon export - Organized dataset delivery with image-wise segmentation files - QA validation report for annotation consistency I understand that high precision is critical, especially for Caries, Implant, Restoration, Pulp, and Canal regions. I can work with CVAT, Labelbox, Supervisely, or similar platforms based on your preference. I’m detail-oriented, reliable, and able to maintain annotation consistency across the dataset. Ready to start immediately and can deliver within the required timeline.

@TiaanvDyk

“This feels like a project where clarity and execution really matter.” Creating precise pixel-level labels for dental X-rays is crucial for accurate clinical assessments. My focus would be on achieving high-precision contours around Caries, Implant, and Restoration areas, ensuring meticulous delineation of Pulp and Canal. I will use my expertise to deliver clean, professional annotations using the Labelbox annotation platform, ensuring seamless integration with COCO JSON format. While I am new to freelancer, I have tons of experience and have done other projects off-site. With a keen eye for detail and a commitment to quality, I am confident in my ability to meet your requirements effectively. I would love to chat more about your project! Regards, Tiaan

@ayushpratapsing5

Analytical Data Annotator specializing in NLP and Computer Vision projects with a eye for detail informed by Experienced in bounding boxes, and sentiment analysis using tools like Labelbox, Amazon Mturk Committed to delivering structured, high-integrity data under tight deadlines to support the development of robust Al solutions."

@Annotator4DXW

Hello, We have experience in medical image annotation and pixel level segmentation projects, including dental radiographs, CT scans, pathology imagery, and AI training datasets requiring clinically consistent labelling. For your dental X-ray segmentation project, we can accurately annotate: • Background • Bone • Tooth (Enamel + Dentin) • Pulp • Canal • Caries • Restoration • Filling • Implant • RCT We understand that caries, implant, and restoration regions require extremely precise boundary delineation for AI training and clinical validation. Our workflow focuses on contour refinement, overlap prevention, and anatomical consistency across all masks. Workflow: • Annotation using CVAT or SageMaker • Pixel level polygon/mask segmentation • Multi stage QA review • Export in COCO JSON or YOLO polygon format Deliverables: 1. Organized folders containing original X-rays and segmentation files by image ID 2. QA validation report including: • Annotation consistency checks • Class validation • Mask overlap verification • Random manual review • Export validation Estimated turnaround time depends on dataset size and image complexity, but we maintain a structured production + QA workflow to ensure accuracy without compromising delivery timelines. Looking forward to discussing the dataset size and requirements. Best regards DXW Team

@sayalip46

I believe I am a strong candidate for this job because I have the right combination of skills, dedication, and a willingness to learn and grow. This proposal delivers a highly meticulous, tooth-by-tooth structural breakdown. We cross-examine every layer of the dental anatomy to support complex treatment planning (such as endodontics, periodontics, and full-mouth rehabilitations) 2. Granular Scope of Work (Tooth-by-Tooth Breakdown)Every report will detail each individual tooth using the following anatomical checklist: Enamel & Dentine Integrity: High-contrast tracking of incipient caries, recurrent decay beneath existing restorations, and attrition. Pulp Chamber & Root Canal Anatomy: Evaluation of pulp stones, calcifications, internal/external resorption, and canal morphology. Periodontal Apparatus: Micro-measurement of crestal bone loss, furcation involvement, and periodontal ligament (PDL) space widening. Periapical & Radicular Regions: Identification of periapical radiolucencies, condensing osteitis, and root fractures. Restorative Margins: Detailed assessment of overhanging margins, open contacts, and structural soundness of crowns/bridges.

@akashr238

Hello, I am very interested in assisting with your dental X-ray segmentation project. I understand that these annotations will be evaluated against clinical ground truth, which makes precision, consistency, and rigorous quality control essential. For this project, I will perform detailed pixel-level segmentation for all required classes, including Bone, Tooth, Pulp, Canal, Caries, Restoration, Filling, Implant, and RCT. Special attention will be given to Caries, Implant, and Restoration boundaries, ensuring accurate contours and clean class separation. Pulp and Canal structures will also be carefully delineated to maintain anatomical consistency across the dataset. My workflow includes: • High-precision manual annotation using professional tools such as CVAT, Supervisely, Labelbox, or your preferred platform. • Multi-stage quality checks to verify label consistency and eliminate overlaps or class conflicts. • Validation of masks before export to ensure compatibility with COCO JSON or YOLO polygon formats. • Organized file structure with image-to-annotation mapping for easy integration into ML pipelines. I would be happy to review a sample image and provide a small test annotation so you can evaluate the quality before proceeding with the full dataset. I look forward to discussing the project and providing an accurate estimate after reviewing the dataset size. Best regards, Akash

@DrZsm

I am already working in this field. Have a knack for digital work and this job combines my professional and hobby work perfectly.

@shibin1049

Hi there, I completely understand what you need. Annotating dental radiographs requires a strong grasp of dental anatomy to properly distinguish structures like Enamel/Dentin, Pulp, and the Canal on grayscale X-rays. I can deliver pixel-level, high-precision instance segmentation masks for all 10 required classes, with a strict focus on your top-priority areas: Caries, Implants, and Restorations. Here is how I will execute this project: Platform Flexibility: Highly proficient in CVAT, Labelbox, and Supervisely—I will use your preferred workspace. Pixel-Perfect Execution: I utilize meticulous brush and polygon tools for high-priority contours to ensure absolute separation and clean boundaries. Standard Deliverables: Perfectly organized folders by image ID, containing original X-rays alongside COCO JSON or YOLO polygon files. Rigorous Quality Assurance: Every export includes a brief QA report summarizing automated validation checks (verifying overlap errors and missing classes) to align perfectly with your clinical ground truth. I am ready to deliver this within 7 days, with zero learning curve. I can also send over a few initial sample annotations early on to ensure our contour tightness is perfectly aligned. Let me know if you'd like me to annotate a sample image first to demonstrate my precision! Best regards, Shibin