3D Pathology Image Analysis Using Patch Aggregation and AI
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Solution Overview
Problem
Traditional two-dimensional histopathology methods fail to fully capture the complex three-dimensional morphological information of human tissues, leading to inaccurate diagnoses and prognoses, and existing 3D imaging techniques face challenges in efficiently analyzing large, feature-rich datasets.
Innovation Solution
A deep learning-based computational pipeline that processes volumetric tissue images to generate patient-level clinical endpoint predictions by encoding features from patches, aggregating them to form volume-level features, and generating predictions without manual annotations, using 3D convolutional neural networks and attention-based aggregation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional two-dimensional histopathology methods are used to examine tissue samples, then the examination process is simple and fast, but the morphological information captured is incomplete and inaccurate
Solution Approach 1:
The patent transitions from two-dimensional histopathology examination to three-dimensional volumetric imaging of tissue samples. By capturing and analyzing tissue morphology in three dimensions, the system preserves complete spatial relationships and structural information that are lost in traditional 2D cross-sections, thereby improving morphological information accuracy while managing the increased complexity through automated AI analysis pipelines
2Reliability
If three-dimensional volumetric imaging is used to capture complete tissue morphology, then diagnostic accuracy is improved, but data processing complexity and time increase significantly
Solution Approach 1:
The patent divides the volumetric tissue image into multiple two-dimensional patches or slices that can be processed independently and in parallel. This segmentation approach allows the large 3D dataset to be handled efficiently by distributing computation across multiple processing units, reducing overall processing time while maintaining complete volumetric analysis for accurate diagnosis
Solution Approach 2:
The patent employs artificial intelligence and deep learning models as intermediaries between the raw volumetric imaging data and the final diagnostic interpretation. These AI systems automatically extract relevant features, identify patterns, and generate diagnostic conclusions, significantly reducing the time required to process complex 3D data while improving diagnostic reliability
3Measurement precision
If manual examination of volumetric tissue images is performed, then detailed analysis is possible, but the process is time-consuming and error-prone
Solution Approach 1:
The patent replaces manual mechanical examination by pathologists with automated artificial intelligence systems that perform image analysis. The AI models process volumetric tissue images, extract morphological features, and generate diagnostic assessments automatically, eliminating human errors and variability while dramatically increasing examination throughput and productivity
Data Source
AI summary
Determining a patient-level clinical endpoint prediction based on analysis of a three-dimensional volumetric image is discussed. One example method includes generating a set of patches from a volumetric image of a tissue sample. The method also includes employing a pretrained feature encoder to extract a set of features from the set of patches. The method additionally includes generating a volume-level feature associated with the volumetric image via an aggregation based on the set of features. The method further includes generating a clinical endpoint prediction based on the volume-level feature.