Casestudy

AI-Powered Fetal Annotation by Logictive Solutions

Logictive Solutions collaborated with Deepecho, a leading medical technology company, on a critical annotation project focusing on identifying and labeling various fetal anatomical structures in ultrasound images. The annotation work involved using polygon, polyline, and ellipse tools to precisely outline structures such as the Myometrium, Ventricles, Choroid Plexus, Cerebellum, Cisterna Magna, Humerus, Heart, Lungs, AF Pocket, Placenta, and Spine. The project aims to create an AI model to track all the above aspects related to Fetal helping in radiologist / specific medical section to smoothen their day to day processes and analyst.

Target Platform:

Web

Outcome Framework

Services We Provided

Data Management

Data Analytics

Project Overview

Fetal Annotation based on Fetal Anatomy Ultrasound Annotation project was designed to support radiologists in identifying key fetal anatomical structures.

Target Scan Period

Ultrasound scan from 18 to 22 weeks of pregnancy.

Critical Role

Accurate identification of listed structures on fetal ultrasound plays a vital role in monitoring fetal development and detecting abnormalities early.

AI Training Requirement

Deepecho’s Fetal Ultrasound Image scanning AI systems required high-quality fetal annotated datasets to train their diagnostic AI models, making this project a crucial step in improving prenatal screening capabilities.

Key Project Objectives

The main objectives of the project were focused on precision, enablement, and standardization.

Produce Precise Annotations

To produce precise and consistent annotations of critical fetal anatomical structures in ultrasound images.

Enable AI Model Accuracy

To enable Deepecho’s AI models to learn and detect these structures accurately, thereby assisting radiologists in faster and more reliable diagnostics.

Standardize Annotation Protocol

To standardize the annotation process for fetal anatomy in the absence of pre-existing detailed guidelines.

Guideline Development and Execution

Logictive Solutions adopted a structured, research-driven, and iterative process to ensure annotation accuracy and project success.

Guideline Development

Since no predefined annotation guidelines were provided, the team conducted extensive research to understand fetal anatomy visibility in ultrasound images. Preliminary guidelines were made based on research and medical expert guidelines and verification, where there were multiple communication session with the radiologists(client).

Annotation Execution

Using specialized annotation tools, our team annotated each ultrasound image, focusing on clarity, boundary accuracy, and structure differentiation.

Verification and Data Structuring

Expert Verification

The guidelines and annotated images were then reviewed and validated through consultations with medical experts/radiologists(client) to ensure anatomical accuracy. Feedback loops were established to refine the guidelines and standardize labeling practices across the dataset.

Data Structuring

Finally, the annotated outputs were converted from unstructured image data into structured formats compatible with Deepecho’s AI systems. This included proper labeling conventions, metadata alignment, and quality assurance checks.

Annotation Tool Application

Different CVAT annotation tools were applied depending on the anatomical structure’s shape to ensure maximum precision during the Fetal Annotation Execution phase.

Polygon Tool

Used for irregular structures like the placenta and lungs.

Polyline Tool

Used for linear structures such as the spine (Initial Stage | Currently Discontinued).

Ellipse Tool

Used for rounded organs like ventricles and cisterna magna.

Project Challenges

To address these challenges, Logictive Solutions implemented several effective strategies:

Lack of Pre-Established Guidelines

There were no standard annotation protocols for fetal ultrasound imagery provided at the start.

Absence of Immediate Medical Expertises

The annotation team initially lacked direct access to radiologists(client), making it difficult to identify less prominent anatomical structures.

Complexity of Ultrasound Images

Ultrasound images often have low contrast and high variability, requiring careful interpretation to avoid mislabeling.

Challenge Mitigation Strategies

To address these challenges, Logictive Solutions implemented several effective strategies

Guideline Creation through Research

The team developed initial annotation guidelines by studying medical imaging literature and referencing fetal anatomy charts.

Medical Expert Consultations

Initially, regular consultations with radiologists(client) were arranged to review ambiguous cases and validate the evolving guidelines.

Iterative Training and Feedback

Fetal Annotation team members underwent targeted training sessions based on expert feedback, which significantly improved accuracy and consistency over time.

Outcomes and Results

The project was successfully completed with high-quality annotations delivered on time. Key outcomes included:

Accurate and consistent Fetal annotation

Of thousands of ultrasound images.

Improved dataset quality

That enabled Deepecho to enhance the performance of their Fetal AI diagnostic models.

Standardized fetal anatomy annotation protocol

That can be reused for future medical imaging projects.

Positive client feedback

That can be reused for future medical imaging projects.

Conclusion and Future Improvements

The Fetal Anatomy Ultrasound Annotation project demonstrated Logictive Solutions’ ability to handle complex medical imaging tasks with precision and adaptability. By combining research, structured workflows, expert consultations, and technical rigor, the team delivered a dataset that contributed to advancing AI-driven prenatal care — ultimately supporting radiologists in ensuring better maternal and fetal health outcomes.

Importance of Clear Guidelines

Developing structured annotation guidelines early in the project can significantly improve team efficiency and reduce revisions.

Scalability of Structured Processes

The framework established in this project can be replicated and scaled for other medical imaging tasks, such as obstetric, cardiac, or organ-specific datasets.