Biotechnology · Workshop

AI-Powered Echocardiography: From 2D Echo to 3D Ventricular Reconstruction

Transforming Clinical Cardiology with Deep Learning, Automated Segmentation, and Geometric Mesh Modeling

ModeVirtual / Online
LevelBeginners
Fee₹2,499

About Workshop

This workshop covers AI pipelines that transform 2D echocardiography data into detailed 3D ventricular models, enabling precise morphological analysis for diagnosing cardiomyopathies, valve disorders, and structural heart disease. Participants will explore machine learning and deep learning methods for image analysis, segmentation, and modeling of cardiac structures. By integrating AI into cardiac imaging, attendees will gain insights into improving diagnostic accuracy, visualizing complex heart anatomy, and applying data-driven approaches in clinical cardiology and biomedical research.

What Participants Will Learn

Provide an understanding of cardiac anatomy and echocardiography fundamentals, including heart chambers, valves, LV wall segments, ultrasound principles and standard echo views.
Introduce participants to AI applications in echocardiography and medical imaging, including CNNs, U-Net architecture, segmentation workflows and clinical image analysis.
Equip participants with practical skills in handling DICOM echo data and cardiac imaging datasets using tools such as Weasis, pydicom, OpenCV and Google Colab.
Enable participants to build and evaluate AI-based LV segmentation and EF quantification models using datasets such as CAMUS and EchoNet-Dynamic.
Explore 3D ventricular reconstruction and end-to-end clinical AI pipelines, including DICOM preprocessing, segmentation, 3D mesh generation, visualization and volume reporting.
Discuss clinical AI deployment, regulatory awareness and career readiness, including FDA SaMD considerations, model deployment with Gradio/ONNX and portfolio-building for medical imaging AI roles.

Structure

Day 1: Cardiac Morphological Anatomy, Ultrasound Physics, and Medical Image Deep Learning Foundations

Anatomical Mapping: Left Ventricular (LV) wall segmentation utilizing the AHA 17-segment model; the critical role of standardized anatomical ground-truth labeling in training robust medical AI models.
Acoustic Physics & Artifacts: Ultrasound wave propagation dynamics ( $frequency vs. depth$ resolution trade-offs); phased vs. matrix array transducers and their relationship to acoustic shadowing and dropouts.
Standard Echocardiographic Planes: Cross-sectional analysis of Parasternal Long-Axis (PLAX), Short-Axis (PSAX), Apical 4-Chamber (A4C), and Apical 2-Chamber (A2C) views.
Hemodynamic Biomarkers: Quantitative estimation of Ejection Fraction ( $$EF$$ ), Stroke Volume ( $$SV$$ ), End-Diastolic ( $$EDV$$ ), and End-Systolic ( $$ESV$$ ) volumes.
DICOM Informatics Deep-Dive: Decoding file structures, parsing hidden metadata tags (Patient/Equipment UID), pixel array extraction, and multi-frame cine-loop data structures.
Computer Vision Fundamentals: Supervised learning paradigms, Convolutional Neural Networks (CNNs), and the specific spatial advantages of the U-Net Encoder-Decoder architecture for pixel-level boundary detection.

Hands-on:

Lab 1: Navigating and parsing real patient echo DICOM arrays using Weasis PACS viewer.
Lab 2: Developing a Python pipeline (pydicom + Matplotlib + OpenCV) to ingest, slice, and visualize multi-frame cine-loops.

Day 1 Exit Outcomes: Successfully ingest clinical DICOM files, programmatically isolate standard views, and mathematically define volumetric biomarkers.

Day 2: Advanced Deep Learning Pipelines, LV Segmentation, and Automated Volumetric Quantification

Endocardial Boundary Tracking: Automated tracking of the endocardial and epicardial contours; isolating challenges like low contrast-to-noise ratio, speckle noise injection, and inter-observer variability.
Data Augmentation for Low-Sample Medical Datasets: Domain-specific transformations including elastic deformations, random flipping, and synthetic speckle noise simulation using Albumentations and MONAI.
Loss Optimization: Balancing class imbalances using customized loss formulations:

$Total Loss = Dice Loss + Binary Cross-Entropy (BCE) Loss$
Spatiotemporal Modeling: Moving beyond static frames—utilizing 3D CNNs and R(2+1)D spatio-temporal architectures for frame-to-frame tracking in dynamic EchoNet-Dynamic cine-videos.
Mathematical Evaluation Metrics: Quantifying model performance using spatial overlap (Dice Similarity Coefficient), boundary distance (Hausdorff Distance), and volumetric error (Mean Absolute Error for $$EF$$ ).
Regulatory & Bioethics: Exploring dataset bias, algorithmic fairness, and FDA Software as a Medical Device (SaMD) compliance pathways for clinical deployment.

Hands-on:

Lab 1: Training an end-to-end MONAI / PyTorch U-Net model on the benchmark CAMUS dataset.
Lab 2: Running video-based deep learning inference via EchoNet-Dynamic to automatically calculate ejection fractions.
Lab 3: Writing a robust custom augmentation script for dynamic cardiac ultrasound profiles.

Day 2 Exit Outcomes: Train a medical segmentation model, validate it using strict clinical AI metrics, and address regulatory algorithmic compliance constraints.

Day 3: 3D Geometric Mesh Reconstruction, Full Deployable Pipelines, and Translational Careers

2D-to-3D Spatial Projection: Mathematical principles of the biplane Simpson's method, multi-planar voxel stacking, and point-cloud generation from isolated 2D semantic masks.
Geometric Mesh Modeling: Implementing 3D Slicer and the SlicerHeart extension to convert segmented point clouds into high-fidelity 3D surface meshes (exportable to .STL / .OBJ formats).
Post-Processing Workflows: Mesh smoothing, decimation, and volumetric computation within professional visualization suites (ParaView and MeshLab).
Pipeline Orchestration: Architecting an integrated production script:

$Raw DICOM —> MONAI Inference —> VTK Surface Rendering —> Automated Volumetric Report$
Model Interoperability & Deployment: Compiling PyTorch models to ONNX Runtime for lightning-fast CPU/GPU execution; building a browser-accessible web application using Gradio; containerization using Docker.
Translation & Careers: Career progression tracks (Clinical AI Engineer, Medical Imaging Scientist, Regulatory Analyst) and portfolio strategy building on GitHub.

Hands-on:

Lab 1: Generating and optimizing a 3D Left Ventricular surface mesh using 3D Slicer and Python VTK.
Lab 2: Running a fully unified Python script that inputs a DICOM file and returns a complete diagnostic volume report.
Lab 3: Shipping a production-ready Gradio web app demo to showcase your model to clinicians.

🎯 Day 3 Exit Outcomes: Convert raw 2D echocardiograms into structured, interactive 3D meshes, bundle the pipeline into a deployable cloud interface, and position your portfolio for top-tier clinical tech roles.

Outcomes

Load, navigate and inspect a real echo DICOM file while understanding basic DICOM structure and metadata.
Identify standard echocardiography views and explain key clinical metrics such as EF, EDV and ESV in simple language.
Understand why U-Net is widely used for cardiac segmentation and describe its role in AI-based echo analysis.
Train a U-Net model on the CAMUS dataset, report Dice score and compare Dice with Hausdorff distance for clinical AI evaluation.
Run EchoNet inference, interpret predicted ejection fraction output and understand the basics of FDA SaMD requirements for AI tools.
Produce a 3D LV mesh, run a complete DICOM-to-EF pipeline and deploy a Gradio EF prediction demo as a portfolio-ready project.

Who Should Attend

Students: Undergraduate or graduate students in biomedical engineering, biotechnology, computer science, data science, healthcare technology, medical imaging, or related fields.
Ph.D. Scholars/Researchers: Research scholars working in medical imaging, AI in healthcare, cardiac imaging, biomedical signal/image processing, clinical AI, or computational healthcare.
Academicians/Faculty: Professors, lecturers, and academic researchers interested in AI-based echocardiography, cardiac image analysis, medical AI workflows, or applied healthcare analytics.
Healthcare & Clinical Professionals: Cardiologists, radiologists, sonographers, clinical researchers, and healthcare professionals interested in understanding AI-assisted echo analysis and EF quantification.
Industry Professionals: AI engineers, data scientists, medical imaging professionals, healthcare technology developers, regulatory professionals, and product teams working on clinical AI or digital health solutions.

AI-Powered Echocardiography: From 2D Echo to 3D Ventricular Reconstruction

About Workshop

Aim

What Participants Will Learn

Structure

Day 1: Cardiac Morphological Anatomy, Ultrasound Physics, and Medical Image Deep Learning Foundations

Hands-on:

Day 2: Advanced Deep Learning Pipelines, LV Segmentation, and Automated Volumetric Quantification

Hands-on:

Day 3: 3D Geometric Mesh Reconstruction, Full Deployable Pipelines, and Translational Careers

Hands-on:

Important Dates

Registration Ends

Workshop Dates

What You Will Gain

Outcomes

Who Should Attend

N shirisha

Department of Biotechnology