Convolutional Encoder-Decoder for Prostate Tumor Detection
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Solution Overview
Problem
Manual analysis of multi-parametric MR images for prostate cancer detection is invasive, time-consuming, and prone to missing subtle cancerous lesions, highlighting the need for automated detection and classification methods.
Innovation Solution
A deep image-to-image network is used for simultaneous detection and classification of prostate tumors in multi-parametric MR images, eliminating the need for feature extraction and allowing direct input of images, with a trained convolutional encoder-decoder generating response maps that peak at tumor locations following a Gaussian distribution for benign and malignant classifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual reading of multi-parametric MR images is performed, then diagnostic accuracy can be maintained, but the process becomes time-consuming and tedious
Solution Approach 1:
The patent replaces the manual mechanical reading process with an automated deep learning system. The convolutional encoder-decoder network automatically processes multi-parametric MR images to detect and classify prostate tumors, substituting the radiologist's manual analysis with an automated computational system that maintains diagnostic accuracy while significantly reducing time consumption.
Solution Approach 2:
The system enables self-service automation where the deep learning network independently performs tumor detection and classification without requiring manual intervention. The network processes the multi-parametric MR images autonomously, generating response maps that identify tumor locations and characteristics, thereby eliminating the tedious manual reading process while preserving diagnostic quality.
2Reliability
If manual reading of multi-parametric MR images is performed, then expert judgment can be applied, but subtle cancerous lesions are difficult to detect even by experts
Solution Approach 1:
The patent transforms the detection task by changing the parameter representation through deep learning feature extraction. The convolutional encoder-decoder network learns optimal feature representations from multi-parametric MR images, converting subtle visual patterns into detectable response map features. This parameter transformation enables reliable detection of subtle cancerous lesions that are difficult to identify through manual visual inspection.
Solution Approach 2:
The deep learning network acts as an intermediary between the raw multi-parametric MR images and the final diagnostic decision. The network processes the complex multi-channel image data through multiple convolutional layers, extracting hierarchical features and generating response maps that highlight subtle tumor characteristics. This intermediary processing enhances the detectability of subtle lesions by translating them into prominent features in the response maps.
3Ease of manufacture
If feature extraction pre-processing is performed, then traditional image analysis can be applied, but the process requires multiple processing steps
Solution Approach 1:
The patent merges multiple processing steps into a single integrated deep learning model. The convolutional encoder-decoder network combines feature extraction, tumor detection, and classification into one unified process. By eliminating the need for separate feature extraction pre-processing steps, the system simplifies the overall workflow while maintaining comprehensive analysis capabilities through the end-to-end learning approach.
Data Source
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AI summary
A method and apparatus for automated prostate tumor detection and classification in multi-parametric magnetic resonance imaging (MRI) is disclosed. A multi-parametric MRI image set of a patient, including a plurality of different types of MRI images, is received. Simultaneous detection and classification of prostate tumors in the multi-parametric MRI image set of the patient are performed using a trained multi-channel image-to-image convolutional encoder-decoder that inputs multiple MRI images of the multi-parametric MRI image set of the patient and includes a plurality of output channels corresponding to a plurality of different tumor classes. For each output channel, the trained image-to image convolutional encoder-decoder generates a respective response map that provides detected locations of prostate tumors of the corresponding tumor class in the multi-parametric MRI image set of the patient.