Systems and methods for automated tumor segmentation in radiology imaging using data mined line annotations

EP4562599A4Pending Publication Date: 2026-03-04MEMORIAL SLOAN KETTERING CANCER CENT +2
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Patent Information

Application Number
EP2023847475
Authority / Receiving Office
EP · EP
Patent Type
Applications
Current Assignee / Owner
Priority Date
2022-07-25
Filing Date
2023-07-24
Publication Date
2026-03-04

AI Technical Summary

Technical Problem

Manual segmentation of pathological lesions in diagnostic imaging is time-consuming, error-prone, and requires extensive labor, limiting the efficiency and accuracy of detecting and characterizing tumors in radiology.

Method used

An AI-driven system that uses unsupervised segmentation techniques to automatically generate and refine pseudo-masks for tumor detection, reducing the need for manually segmented training images by converting line annotations into bounding boxes and iteratively improving segmentation quality through self-refinement and reconciliation processes.

Benefits of technology

The system enhances the precision of tumor segmentation, decreases the time required for training data encoding, and reduces the reliance on extensive manual annotation, enabling faster and more accurate detection and characterization of tumors in radiology imaging.

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Abstract

Systems and methods for segmenting pathological features are disclosed. The system can receive a plurality of training images and corresponding data mined or separately introduced line annotations. The system can train a component thereof to generate bounding boxes upon an image of interest based on the training images and the bounding boxes converted from the mined line annotations. The system can detect and refine the segmentations of a pathological feature on the image of interest. The system can reconcile various images, or portions thereof, between the refinement or other processes performed by the system.
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