GPU Cluster Parallel Processing for Multi-Resolution Medical Image Analysis
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Solution Overview
Problem
The existing methods for analyzing medical images of tissues are time-consuming, especially when processing multiple multi-resolution images simultaneously, leading to heavy workloads for processing units and inconsistent diagnoses among pathologists.
Innovation Solution
A system utilizing a cluster of Graphical Processing Units (GPUs) to analyze histo-pathology medical images by dividing images into segments, scheduling sub-jobs across multiple GPUs for parallel processing, and collating results efficiently, allowing for asynchronous data transfer and prioritization of jobs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple multi-resolution medical images are processed simultaneously using traditional CPU-based methods, then diagnostic consistency improves, but processing time increases significantly
Solution Approach 1:
The patent divides medical images into multiple tiles or segments that can be processed independently and in parallel. Each image is segmented into manageable chunks that can be assigned to different processing units, enabling simultaneous processing of multiple images while maintaining diagnostic quality through consistent analysis of each segment.
Solution Approach 2:
The patent replaces traditional CPU-based sequential processing with GPU-based parallel processing architecture. The mechanical sequential operation of CPUs is substituted with the parallel computational capability of GPUs, which can handle multiple image processing tasks simultaneously, dramatically reducing processing time while maintaining diagnostic consistency.
2Device complexity
If a single processing unit handles multiple medical images, then system complexity reduces, but workload and processing time increase
Solution Approach 1:
The patent merges multiple processing units (GPUs) into a coordinated parallel processing system. By combining the computational power of multiple GPUs that work simultaneously on different segments of images, the system achieves high processing throughput while managing complexity through unified job scheduling and result aggregation mechanisms.
Solution Approach 2:
The patent transitions from single-dimensional sequential processing to multi-dimensional parallel processing by utilizing multiple GPUs working simultaneously on different image segments. This dimensional expansion of processing capacity allows the system to handle multiple images concurrently, dramatically improving productivity without proportionally increasing system complexity.
3Measurement precision
If each image is processed sequentially one at a time, then processing accuracy maintains, but overall analysis speed decreases
Solution Approach 1:
The patent segments images into tiles that can be processed in parallel while maintaining the accuracy of sequential processing. Each tile is processed independently with the same analytical algorithms, ensuring processing accuracy is maintained while the overall analysis speed increases through simultaneous processing of multiple tiles across different images.
Data Source
AI summary
A method and system analyzes multi-resolution medical images. The method includes receiving one or more multi-resolution medical images of one or more tissues. Each multi-resolution medical image is divided into a plurality of segments. Thereafter, each segment of the plurality of segments of each multi-resolution medical image is processed in parallel, utilizing one or more computation nodes, wherein each computation node includes one or more Graphical Processing Units (GPUs). The results corresponding to each segment of a multi-resolution medical image are collated, and a collated report is displayed.