Blur-robust nuclei segmentation via deep learning deblurring
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Solution Overview
Problem
Automated nuclei segmentation from immunofluorescence microscopic images is challenging, especially with poor quality images due to defocus and motion blur, leading to misdiagnosis and increased computational resources for de-focusing and segmentation tasks.
Innovation Solution
A method that combines deblur and semantic segmentation using a single network, generating a deblurred image and then performing nuclei segmentation, with post-processing to separate touching cells, utilizing a deep learning system with an encoder-decoder architecture and total loss value optimization through backpropagation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Quality Control modules reject out-of-focus images, then image quality is maintained, but computational resources are wasted and analysis is delayed
Solution Approach 1:
The system performs deblurring as a preliminary action before segmentation. By pre-processing the image to restore sharpness, the system enables subsequent analysis to proceed with improved accuracy, avoiding the need to reject images entirely and thus maintaining both quality and productivity.
Solution Approach 2:
The deblurring module acts as an intermediary between the blurred input image and the segmentation analysis. This intermediate processing step recovers the underlying sharp image structure, allowing the segmentation algorithm to work effectively on degraded images without requiring image rejection.
2Reliability
If deblurring and segmentation are performed as separate tasks, then each task can be optimized independently, but computational resources and processing time increase
Solution Approach 1:
The patent combines deblurring and segmentation into a unified deep learning framework where a single neural network performs both functions simultaneously. This merging of tasks shares computational resources and avoids the redundant processing that would occur if separate models were used, reducing overall energy consumption while maintaining optimization for both tasks.
Solution Approach 2:
The deep learning model is designed with multi-functionality, serving both as a deblurring algorithm and a segmentation algorithm. This universal approach allows the system to handle image quality restoration and object identification in a single computational pass, efficiently utilizing resources compared to specialized separate tasks.
3Measurement precision
If deep learning models are trained only on sharp images, then training data quality is high, but the model cannot handle blurred images from real-world microscopy
Solution Approach 1:
The training process incorporates parameter changes by introducing blur levels as a variable. The model is trained on synthetic blurred images with controlled degrees of blur, allowing it to learn and adapt to different image quality conditions. This enables the model to maintain high measurement precision while developing adaptability to handle the full range of real-world microscopy image qualities.
Solution Approach 2:
The system creates synthetic blurred images by applying blur operators to sharp reference images during the training process. This copying approach allows the model to learn from controlled blur conditions without requiring actual blurred microscope images, enabling the model to generalize to real-world blurred images while maintaining training data quality.
Data Source
AI summary
A method, computer program, and computer system is provided for image processing. Data corresponding to an out-of-focus image is received. A first image is generated based on segmenting the received data. A second image is generated based on sharpening the received data corresponding to the out-of-focus image. A third image corresponding to one or more predetermined objects (e.g., cells) in the out-of-focus image is generated based on post-processing the generated first and second images.