Convolutional Ladder Network for 3D Medical Image Segmentation
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Solution Overview
Problem
Current medical image segmentation methods, particularly for large 3D images like lung CT scans, face challenges with high computational costs and loss of spatial information, leading to over-smoothed boundaries and inefficiencies in processing time.
Innovation Solution
A multi-level learning network with a convolutional ladder architecture is employed, which cascades convolution blocks at multiple levels, concatenates feature maps across different resolutions, and uses up-sampling to maintain spatial resolution, reducing computational complexity and improving segmentation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a sliding window approach is used for pixel/voxel classification in CNN, then segmentation accuracy can be achieved, but computational cost becomes prohibitive for large images
Solution Approach 1:
The image is divided into multiple patches that are processed in parallel. Each patch is classified independently using the sliding window approach, but the parallel processing significantly reduces the overall computational time compared to processing the entire image sequentially.
Solution Approach 2:
Instead of processing the entire image at once or requiring full image context for each pixel, the method processes only local patches. This partial action approach reduces computational complexity while still achieving accurate segmentation for each region through localized feature extraction.
2Loss of information
If pooling layers are used to increase receptive fields in FCN, then context information is obtained, but spatial resolution is reduced causing over-smoothed boundaries
Solution Approach 1:
The network uses dilated convolutions with increasing dilation rates at different levels, creating a multi-scale feature extraction approach. This allows the receptive field to expand in terms of coverage area without reducing spatial resolution through pooling operations, thereby maintaining both context information and boundary sharpness.
Solution Approach 2:
The dilation rate parameter is varied across different levels of the network architecture. By changing this parameter, the network achieves different receptive field sizes without using pooling layers, thus preserving spatial resolution while still capturing contextual information at multiple scales.
3Manufacturing precision
If U-Net architecture with up-sampling is used to preserve spatial resolution, then boundary accuracy is maintained, but processing time increases for large 3D medical images
Solution Approach 1:
The network processes the 3D medical image by dividing it into multiple 2D slices or patches that can be processed independently and in parallel. This segmentation approach maintains boundary accuracy through the multi-level feature extraction while significantly reducing the computational burden and processing time for large volumetric data.
Solution Approach 2:
The network dynamically adjusts the level of detail processed at each stage. Coarse segmentation is performed first on down-sampled representations, and then progressively refined at higher resolutions only where needed, rather than uniformly processing all regions at full resolution. This dynamic approach maintains accuracy while improving efficiency.
Data Source
AI summary
Embodiments of the disclosure provide systems and methods for segmenting an image. An exemplary system includes a communication interface configured to receive the image acquired by an image acquisition device. The system further includes a memory configured to store a multi-level learning network comprising a plurality of convolution blocks cascaded at multiple levels. The system also includes a processor configured to apply a first convolution block and a second convolution block of the multi-level learning network to the image in series. The first convolution block is applied to the image and the second convolution block is applied to a first output of the first convolution block. The processor is further configured to concatenate the first output of the first convolution block and a second output of the second convolution block to obtain a feature map and obtain a segmented image based on the feature map.