Medical Image Segmentation Using Geodesic Distance Maps

The patent segments the CNN architecture into encoder and decoder pathways, where the encoder performs down-sampling for feature extraction and the decoder performs up-sampling for resolution recovery. This segmentation allows each part to specialize: the encoder focuses on computational efficiency while the decoder restores segmentation accuracy through transposed convolutions and skip connections.

The patent implements nested feature pyramids where features at different scales are hierarchically organized and integrated. The encoder generates features at multiple down-sampled levels, and the decoder nestles these features back together through up-sampling, creating a nested structure that preserves both coarse contextual information and fine spatial details for accurate segmentation.

The patent dynamically adjusts the receptive field through the hierarchical encoder-decoder structure. Instead of using fixed dilated convolution layers, the network dynamically expands its receptive field by progressively down-sampling in the encoder and then recovering resolution in the decoder, allowing flexible adaptation to different scale requirements without increasing structural complexity.

The patent transitions from the spatial dimension to the feature dimension by using channel concatenation and fusion operations. Instead of expanding spatially through dilated convolutions, the network enriches features by combining information from multiple encoder levels at different scales, achieving comprehensive receptive field coverage through feature space integration rather than spatial expansion.

The patent implements self-service by training the CNN to automatically learn spatial regularization patterns during the main segmentation training process. The network learns to preserve edges and spatial structures through the encoder-decoder architecture and skip connections, eliminating the need for separate CRF parameter tuning. The system serves its own spatial regularization needs through learned features rather than manual parameter adjustment.

The patent replaces the mechanical CRF system with Gaussian potentials and manual parameter tuning with a learned neural network system. Instead of using fixed mathematical models requiring parameter adjustment, the network learns spatial relationships and edge preservation directly from data through back-propagation, substituting the mechanical optimization process with adaptive learning.

The patent creates a dynamic architecture where the encoder-decoder pathways can adaptively adjust feature extraction and reconstruction based on input complexity. The network dynamically routes information through skip connections at different levels, allowing flexible handling of complex cases without being constrained by fixed Gaussian potentials. This dynamic structure provides the adaptability needed for diverse medical imaging scenarios.

The patent changes the fundamental parameters of the model from fixed Gaussian potential functions to learnable convolutional filters and feature transformations. Instead of being constrained by the mathematical form of Gaussian potentials, the network learns appropriate spatial relationships through trainable parameters, enabling adaptation to complex cases while maintaining end-to-end automatic learning through gradient-based optimization.