Random Expert Sampling for Deep Learning NCCT Stroke Segmentation
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Solution Overview
Problem
Current methods for segmenting acute ischemic stroke on non-contrast CT (NCCT) suffer from high inter-observer variability due to low contrast-to-noise ratio and reliance on expert annotations, leading to uncertainty and inefficiencies in treatment decisions.
Innovation Solution
A deep learning model trained using random expert sampling of neuroradiologist segmentations, employing a U-Net architecture with nnUNet configuration, to generate accurate segmentation masks of acute ischemic stroke.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rule-based algorithms or expert neuroradiologists are used to segment ischemic stroke on NCCT, then segmentation can be performed, but high interobserver variability occurs due to low contrast-to-noise ratio
Solution Approach 1:
A deep learning model is introduced as an intermediary between the NCCT images and the segmentation output. The model learns from multiple expert annotations and synthesizes a consensus segmentation that surpasses individual expert agreement, effectively using the deep learning system as a mediator to resolve the variability issue.
Solution Approach 2:
Multiple expert annotations are merged through the deep learning model to create a consensus ground truth. By combining information from multiple experts rather than relying on a single expert or simple majority voting, the system achieves higher reliability and reduces interobserver variability.
2Measurement precision
If multiple expert segmentations are collected to approximate the distribution of interpretations, then ground truth accuracy improves, but resource consumption and time increase
Solution Approach 1:
Instead of requiring complete annotations from all experts for every training example, the system uses a sampling approach where only a subset of expert annotations is needed. This partial action approach maintains ground truth accuracy while significantly reducing the resource burden of collecting and processing multiple complete expert annotations.
Solution Approach 2:
The deep learning model creates a synthetic consensus ground truth by learning from multiple expert annotations during training. Once trained, the model can generate consistent, high-quality segmentations without requiring continuous input from multiple experts, effectively copying the wisdom of multiple experts into a single deployable system.
3Ease of manufacture
If synthetic target lesions or co-registered target lesions from MR imaging are used for training, then segmentation training becomes feasible, but dependency on co-registration quality and imaging correlates limits accuracy
Solution Approach 1:
Multiple expert annotations serve as an intermediary ground truth that directly corresponds to the NCCT images without requiring co-registration with other imaging modalities. This eliminates the accuracy limitations associated with co-registration quality and imaging correlate uncertainty while maintaining training feasibility.
4Measurement precision
If STAPLE algorithm or other advanced fusion methods are used to combine multiple expert annotations, then ground truth convergence improves, but the algorithms do not converge when labels greatly differ
Solution Approach 1:
The deterministic mechanical fusion algorithms (STAPLE, SIMPLE) are replaced with a probabilistic deep learning approach. The model learns to handle label variability through probabilistic modeling during training, allowing it to converge even when expert labels greatly differ, unlike deterministic fusion algorithms that fail under such conditions.
Solution Approach 2:
The system changes from fixed fusion rules (majority voting, STAPLE) to learned probabilistic parameters. The deep learning model adapts its internal parameters to handle the variability in expert annotations, allowing flexible convergence behavior that accommodates cases where expert labels greatly differ.
Data Source
AI summary
A method is described for generating segmentation masks to assist in identification of acute ischemic stroke. The method includes performing by a non-contrast computed tomography scan to produce a computed tomography image; inputting the computed tomography image to an input layer of a deep learning neural network; and outputting a segmentation mask of acute ischemic stroke from an output layer of the deep learning neural network, wherein the segmentation mask of acute ischemic stroke is generated in response to the computed tomography image input to the deep learning neural network. The deep learning neural network is trained with ground truth non-contrast computed tomography images and corresponding segmentation masks of acute ischemic stroke, wherein multiple segmentation masks of acute ischemic stroke for each of the non-contrast computed tomography images are randomly sampled for training.