Medical Image Segmentation Using Uncertainty-Guided Curriculum Learning
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Solution Overview
Problem
Annotating large amounts of medical images for training deep neural networks in medical image segmentation is difficult, expensive, and time-consuming, posing a challenge for effective training.
Innovation Solution
A cross-domain segmentation framework using uncertainty-guided curriculum learning, which involves generating synthetic images, applying transformations, and computing uncertainty to determine suitable images for training, progressively training the network with teacher-student consistency and semi-supervised approaches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large amounts of annotated training data are used to train deep neural networks for medical image segmentation, then segmentation accuracy is improved, but annotation cost and time consumption increase significantly
Solution Approach 1:
The patent generates synthetic medical images that copy the essential characteristics of real medical images but do not require manual annotation. These synthetic images are created by transforming real annotated images through domain adaptation models, allowing the system to obtain large quantities of training data without the time-consuming manual annotation process while maintaining sufficient accuracy for training segmentation networks
Solution Approach 2:
The system uses uncertainty-guided curriculum learning where the model automatically identifies and focuses on uncertain regions during training. The teacher network generates pseudo-labels for uncertain samples, and the student network learns from these self-generated labels, enabling the system to train effectively without external annotation intervention for all data
2Measurement precision
If large amounts of annotated training data are used to train deep neural networks for medical image segmentation, then segmentation accuracy is improved, but annotation expense increases significantly
Solution Approach 1:
The patent creates synthetic medical images that replicate the anatomical and diagnostic characteristics of real medical images without requiring expert annotators. The domain adaptation model transforms real images into synthetic versions that maintain diagnostic quality while eliminating the need for expensive manual annotation resources
Solution Approach 2:
The patent introduces a teacher network as an intermediary that automatically generates pseudo-labels for training data. This teacher network acts as a mediator between the raw medical images and the student segmentation network, providing annotated data without requiring human experts, thereby reducing annotation expenses while maintaining training quality
3Quantity of substance
If synthetic images are generated and transformations are applied to create augmented images, then training data quantity is increased, but computational complexity increases
Solution Approach 1:
The patent performs domain adaptation and synthetic image generation as preliminary actions before the main training process. By pre-transforming real medical images into synthetic images and pre-computing uncertainty metrics, the system reduces the computational burden during subsequent training epochs while still achieving diverse training data quantity
Solution Approach 2:
The patent implements dynamic curriculum learning where the difficulty of training samples is adjusted based on uncertainty metrics. The system dynamically selects which images to annotate and which to use as pseudo-labeled data, optimizing the balance between training data quantity and computational complexity by focusing resources on the most informative samples
Data Source
AI summary
Systems and methods for training a machine learning based segmentation network are provided. A set of medical images, each depicting an anatomical object, in a first modality is received. For each respective medical image of the set of medical images, a synthetic image, depicting the anatomical object, in a second modality is generated based on the respective medical image. One or more augmented images are generated based on the synthetic image. One or more segmentations of the anatomical object are performed from the one or more augmented images using a machine learning based reference network. An uncertainty associated with segmenting the anatomical object from the respective medical image is computed based on results of the one or more segmentations. It is determined whether the respective medical image is suitable for training a machine learning based segmentation network based on the uncertainty. The machine learning based segmentation network is trained based on 1) the suitable medical images of the set of medical images and 2) annotations of the anatomical object determined using a machine learning based teacher network.