AI CBCT Lesion Segmentation Plugin

@intelliresponse

Hello, I can build a 3D Slicer extension for CBCT periapical lesion detection, segmentation, measurement, and longitudinal comparison using research-grade imaging workflows. I’ll deliver: -3D Slicer scripted Python extension for Windows/macOS -DICOM CBCT import workflow for Sirona GALILEOS / SIDEXIS exports -Pre-trained PyTorch/MONAI lesion segmentation integration -Segment Editor-based manual correction and mask approval -Automatic lesion volume calculation in mm³ -CSV export of lesion metrics and study metadata -NIfTI/labelmap mask export for reuse -Baseline/follow-up registration and side-by-side or overlay comparison -Report showing absolute and percentage volume change I have experience with medical imaging, DICOM/CBCT workflows, 3D Slicer extensions, MONAI/PyTorch segmentation, registration, and quantitative analysis. Ready to review your annotated volumes and define the fastest path to a working research MVP.

@kamran2012

I can help with this, I will build your 3D Slicer extension — DICOM import, MONAI-based lesion detection, interactive mask refinement, and automated volume reporting with CSV export. For follow-up comparison, I will use ITK rigid registration to align scans and compute absolute and percentage volume change. Questions: 1) What voxel spacing do your GALILEOS CBCT exports typically use? 2) How many annotated volumes are available for model validation? This bid is an initial estimate — I will confirm the final cost and timeline once we have walked through the complete requirements together. Send me a message and we can go over the details. Best regards, Kamran

@Herculesz

Hi, this project requires precise integration of medical imaging workflows with AI-driven segmentation, which aligns well with my experience extending complex software platforms and integrating deep learning models. The primary engineering challenge lies in robust orchestration of DICOM ingestion, model inference, and interactive segmentation refinement while maintaining clinical accuracy and usability. I usually structure such systems by separating the ingestion, inference, and user interaction layers, ensuring modularity and maintainability. My work on Custom Feature Development & Integration involved similar extension and integration tasks with a focus on clean, testable code. I also developed AI-driven content generation platforms that required automated processing pipelines combined with human-in-the-loop approval, which parallels the lesion mask refinement and volume calculation here. I approach these solutions with production-grade reliability in mind, incorporating fallback mechanisms and validation checks to ensure consistent output quality. I can start by outlining the DICOM import pipeline and sketching the segmentation and registration architecture for your review. Thanks, Hercules

@toriquldev123

Hello dear, Greetings from MD. Toriqul Islam! We are a dedicated Web Design & Development team with over 10+ years of industry experience. I’m Engineer Toriqul Islam, an experienced Computer Science & Engineering graduate from RUET. We specialize in building modern, scalable, and user-friendly digital solutions tailored to business needs. What I Offer We help businesses grow online by delivering: • Clean, modern, and responsive website designs • High-performance and scalable web applications • User-focused UI/UX for better engagement and conversion My Technical Expertise We work across a wide range of technologies, including: • Frontend: HTML5, CSS3, Bootstrap, JavaScript, jQuery, Angular, React • Backend: Node.js, PHP, Laravel, .NET, CodeIgniter, Ruby on Rails, Python • CMS & Platforms: WordPress • Database: MySQL, MongoDB • Mobile Development: React Native, Flutter, and more Why choose me? ✔️ Clean, optimized, and well-documented code ✔️ Reusable and scalable components ✔️ On-time delivery with complete requirement fulfillment We are confident in our ability to turn your ideas into a powerful digital product. Let’s discuss your project and make it a success. Looking forward to working with you! Best Regards, Md. Toriqul Islam

@maxscender

I can help you. I will develop a Python-based 3D Slicer scripted module to ensure seamless cross-platform installation without compilation hurdles. My approach involves using a custom MONAI-based inference engine to pipe your model’s predictions directly into the Segment Editor, allowing for immediate manual refinement. For the longitudinal component, I will implement a registration workflow using Slicer’s BRAINSFit or Elastix modules to align follow-up scans to the baseline, enabling automated delta-volume calculations. All metrics will be extracted via the Segment Statistics API to ensure mm³ precision and formatted into a CSV export. This architecture prioritizes Slicer’s native DICOM database handling to keep the Sirona import process frictionless.

@OlektraGroup

Hi, You need a 3D Slicer extension to automate the segmentation of periapical lesions in Sirona CBCT volumes, specifically enabling longitudinal volumetric analysis and clinical-grade reporting. I recently developed a pipeline for MRI reconstruction and deep learning-based pattern recognition, where I handled complex DICOM-to-NIfTI workflows and custom segmentation refinement. For your project, I propose using a **MONAI-based 3D U-Net architecture** optimized for small-lesion detection, integrated directly into Slicer via a Python scripted module. I have previously achieved a 92% dice overlap on medical imaging tasks and am confident in meeting your <20% manual correction threshold. My experience converting models to ONNX and deploying TFLite ensures your plugin will remain portable across Windows and macOS. Do you have the ground-truth segmentations stored as RT-Structs or raw labelmaps?

@JawadIT

Milestone 1 will include: - 3D Slicer extension setup - DICOM import (GALILEOS/SIDEXIS) - AI-assisted lesion segmentation (initial mask + interactive refinement) - Lesion volume calculation (mm³) - Mask export (NIfTI/labelmap) - CSV export of measurements - End-to-end workflow demonstration on one CBCT case

@teodorgeorge

Hello, I can build a 3D Slicer extension for your CBCT periapical lesion workflow, focusing first on accurate AI-assisted detection and segmentation from Sirona GALILEOS / SIDEXIS DICOM volumes. I have experience with medical imaging pipelines, DICOM/NIfTI handling, Python/C++ Slicer modules, MONAI/PyTorch model integration, registration, mask editing, volume measurement, and CSV reporting. The tool can use Slicer’s Segment Editor for quick review and correction, export clean masks and metrics, and support baseline vs follow-up comparison with absolute and percentage lesion volume change. I am ready to begin immediately and would be happy to discuss the project in further detail. Thanks, Teo

@antonk52

As a seasoned developer with substantial experience in software architecture, C/C++ programming with web and mobile development expertise, I'm here to deliver the AI CBCT Lesion Segmentation Plugin you need utilizing my skill set. With prior projects associated with image analysis and medical domain, I am well-versed in leveraging open-source libraries for building powerful, yet user-friendly and robust tools like the one you described. Given the project's objectives of high accuracy, DICOM compatibility, pre-trained model integration, interactive editing tools and automatic volume calculation, I will use my knowledge of Python (including libraries like ITK-Snap bridges) to create an efficient 3D Slicer extension for you. Finally, it's worth mentioning that I am not only about developing functional software; ensuring a frictionless user experience is paramount. Hence, I'll ensure that our plugin seamlessly installs through Slicer’s Extension Manager on both Windows and macOS fulfilling all acceptance criteria. Trust me with your AI CBCT Lesion Segmentation Plugin project and let's achieve clinically acceptable boundaries with unmatched efficiency!

@Noorulamin331

Hi, I can build your 3D Slicer extension for periapical lesion analysis with AI-assisted segmentation, volumetric measurement, and longitudinal comparison. Technical approach: I will develop a Python scripted module for Slicer that integrates a pre-trained MONAI or nnU-Net model for initial lesion detection . The module will use Slicer's Segment Editor for manual refinement . For longitudinal registration, I will use the General Registration (Elastix) module with labelmap masking to focus on the periapical region . Key deliverables: DICOM import with automated lesion suggestion, interactive mask editing via Segment Editor, automatic volume calculation (mm³) with CSV export, saving masks as NIfTI/labelmap, and side-by-side baseline/follow-up comparison with percentage change report. The extension will be installable through Slicer's Extension Manager on Windows and macOS. Why this works: Volumetric analysis of periapical lesions using CBCT is clinically validated . MONAI and nnU-Net have proven effective for dental CBCT segmentation and integrate well with Slicer . Acceptance criteria: ≤20% manual correction needed, volume accuracy within ±5% of ground truth, and complete baseline/follow-up change report. Share your annotated CBCT volumes and I will start immediately. Looking forward to working with you. Best regards,

@lakshithap1

As a passionate and motivated Software Engineer, I bring a unique blend of skills and expertise to your project that make me the perfect candidate for developing your AI CBCT lesion segmentation plugin. Having worked on dynamic web, mobile, and desktop applications, I am well-versed in programming tasks such as creating user-friendly interfaces and seamless integrations - key components you've highlighted as essential for your project. In particular, my proficiency in C++ programming aligns perfectly with your need for a 3D Slicer extension that's easily integrated and compatible across Windows and macOS. Moreover, my experience with software architecture will contribute greatly to ensuring a comprehensive approach to develop an enhanced workflow for your lesion analysis system. While I am still fairly early in my career, this internship opportunity is precisely what I am seeking to further sharpen my skills and gain real-world experience. The unique nature of your project requires not only programming expertise but also the ability to perseverate on highly detailed work while keeping an eye on long-term goals – a balance that I am confident I can deliver on. Combining this with my consistent dedication to high quality work and continuous improvement, my commitment to providing efficient and scalable solutions aligns powerfully with your project goals.

@pedrowilberthr2

Hello, I’m excited to propose a focused, production-ready 3D Slicer extension that streamlines AI-assisted periapical lesion analysis from Sirona GALILEOS/SIDEXIS CBCT DICOM volumes. The core deliverable is a robust, research-grade tool built as a Python-scripted module (with optional C++ integration) that prioritizes accurate lesion detection and precise segmentation, followed by seamless downstream analyses. Key approach: - Frictionless DICOM import into Slicer, then inference with a pre-trained deep learning network for initial lesion masks. The user can quickly refine or approve masks using Slicer’s Segment Editor. - Automatic calculation of total lesion volume in mm³, plus an exportable CSV of all metrics for easy record-keeping and longitudinal studies. - Saving of clean segmentation masks (NIfTI or labelmap) for reuse, enabling rapid reruns and multi-timepoint comparisons. - A dedicated longitudinal workflow: baseline and follow-up scans can be registered and compared side-by-side or overlaid, with absolute volumes and percentage changes reported automatically. - Leverage open-source libraries (MONAI, PyTorch, ITK, and existing Slicer bridges) to accelerate development while maintaining high accuracy and reproducibility. All deliverables map directly to your acceptance criteria, and I’ll provide a test dataset with ground-truth masks to validate ≤20% manual correction and ±5% volume accuracy, plus a straightforward Extension Manager installation path for Windows and

@Lazarone1

Hello, thank you for your project. I have built 3D Slicer extensions with PyTorch and MONAI for medical image segmentation. I will integrate pre-trained deep learning model for periapical lesion detection, Segment Editor for manual correction, volume calculation (mm³), baseline vs follow-up registration with longitudinal change report. Export to NIfTI/labelmap. Extension will work on Windows and macOS. Two questions: Do you have annotated training data or only test volumes? What is the expected voxel spacing of your CBCT volumes? Thank you and I look forward to working with you.

@codeandcommand

The Sirona GALILEOS DICOM format has metadata quirks that trip up standard parsers. I would build the pipeline in Python using SimpleITK for volume loading and a 3D UNet for lesion detection, wrapped as a 3D Slicer plugin. About 7 to 10 days depending on the number of lesion classes you need segmented. These numbers are based on the post as written. Once we walk through the full scope, including output format and volume count, both may adjust. I can start today. Want to jump on a quick call?

@juanestebana17

Hello, With extensive experience in medical image processing and AI-enhanced segmentation, I am confident in developing a precise and user-friendly 3D Slicer extension that meets your needs. I will facilitate seamless DICOM import, integrate a pre-trained deep learning model for lesion detection, and enable efficient manual refinements and automatic volume calculations. Your vision of a tool that compares baseline and follow-up scans will be realized with reliable registration and metrics export. Could you specify which deep learning models you prefer or have in mind for lesion detection and registration? Thanks, Juan Aponte

@DngPhgNam

⭐⭐⭐⭐⭐ ✅Hi there, hope you are doing well! I have developed medical imaging tools, including 3D Slicer extensions that integrate deep learning models, enabling effortless lesion detection and segmentation with user-friendly refinement. The most critical part to ensure success is maintaining high accuracy in lesion segmentation while streamlining the user workflow for smooth interaction and reliable volume calculation. Approach: ⭕ Use Python scripted module for smooth integration with 3D Slicer. ⭕ Integrate a pre-trained deep learning model using MONAI and PyTorch for initial lesion segmentation. ⭕ Implement interactive editing tools with Slicer’s Segment Editor for easy mask refinement. ⭕ Automate volume calculations and export results to CSV. ⭕ Enable saving of segmentation masks in NIfTI or labelmap formats. ⭕ Develop side-by-side comparison mode with registration between baseline and follow-up scans. ❓ Could you please share the preferred programming language between Python and C++ for this extension? ❓ Are there particular pre-trained models you prefer or should I select from MONAI's offerings? ❓ What is your sample dataset size for training and testing? I am confident I can deliver a highly accurate, user-friendly 3D Slicer extension that meets your acceptance criteria promptly. Thank you for considering my proposal. I look forward to collaborating with you. Best regards, Nam

@GenePauly

Hi there, This is Gene from Luxembourg. CBCT lesion segmentation usually breaks when preprocessing, voxel spacing normalization and registration are not tightly coupled with inference, leading to drift in longitudinal comparison. I would implement a Slicer scripted module that runs a unified pipeline from DICOM import to model inference, followed by correction in Segment Editor and consistent volume computation. I’d integrate a MONAI/PyTorch-based model with ITK registration and Slicer segmentation APIs to ensure stable baseline vs follow-up alignment and reproducible mm³ metrics. I’ve built complex real-time and data-heavy UI systems in LiveJasmin and Oranum where latency, consistency and interactive tooling were critical. Delivery: 4 days. What format is your pre-trained model currently in for deployment inside Slicer? I can start right away

@carlosd178

Hello, As a result of a detailed review of your project requirements, I fully understand the need for a 3D Slicer extension focused on accurate CBCT lesion detection, segmentation, and longitudinal measurement. I have experience building medical imaging, computer vision, and AI-assisted analysis workflows and I'm available to start immediately. I bring strong expertise in Python, C++, 3D Slicer module development, MONAI/PyTorch, DICOM/CBCT processing, image registration, segmentation, and medical image analysis with over 10 years of experience. One of the key challenges here is integrating the model cleanly into Slicer while preserving voxel spacing, mask accuracy, volume calculation in mm³, and baseline/follow-up registration consistency. I can build a Slicer extension with DICOM import, AI mask suggestion, Segment Editor refinement, volume metrics, CSV export, NIfTI/labelmap saving, and overlay comparison for percentage change tracking. I have a couple of quick questions. • Do you already have a preferred pre-trained lesion model, or should model selection/fine-tuning be included? • Should the first version be a Python scripted module to speed up delivery and simplify Windows/macOS installation? I would be glad to discuss further details and am ready to start immediately. Looking forward to hearing from you. Best regards, Carlos

@HamparProd

Hi, The workflow is clear: import DICOM CBCT volumes, run pre-trained lesion detection, allow interactive corrections, and compute accurate volumes with baseline vs follow-up tracking. I’d structure the plugin to integrate a MONAI/PyTorch model with Slicer’s Segment Editor for seamless editing. The key is maintaining segmentation accuracy within clinically acceptable bounds while ensuring volume measurements and longitudinal comparisons are precise and reproducible. I’ve developed similar medical imaging plugins and segmentation tools with Python/C++ in 3D Slicer for research applications. I can deliver a cross-platform Slicer extension that meets your accuracy targets, is easy to install, and produces reliable quantitative outputs immediately.