ψ-Net: Stacking Densely Convolutional LSTMs for Sub-Cortical Brain Structure Segmentation.

Area of Science:

• Neuroimaging
• Medical Image Analysis
• Artificial Intelligence

Background:

• Accurate sub-cortical brain structure segmentation is crucial for diagnosing neuropsychiatric disorders.
• Existing automatic segmentation methods face challenges due to ambiguous boundaries, complex anatomy, and shape variations.
• Deep learning approaches, particularly Convolutional Neural Networks (CNNs), show promise but require architectural improvements for this task.

Purpose of the Study:

• To develop a novel deep network architecture, Ψ-Net, for improved sub-cortical brain structure segmentation.
• To selectively aggregate features and enhance information propagation within a CNN for better segmentation performance.
• To address the challenges of ambiguous boundaries and anatomical complexity in sub-cortical brain segmentation.

Main Methods:

• Proposed a novel deep network architecture named Ψ-Net.
• Introduced a densely convolutional Long Short-Term Memory (DC-LSTM) module for selective feature aggregation at each CNN stage.
• Stacked multiple DC-LSTM modules progressively from deep to shallow layers to enrich feature maps with contextual information.

Main Results:

• The proposed Ψ-Net demonstrated superior performance in sub-cortical brain structure segmentation.
• Experimental results on two benchmark datasets showed favorable comparisons against state-of-the-art methods.
• The DC-LSTM module effectively promoted feature discriminativeness and information propagation.

Conclusions:

• The novel Ψ-Net architecture offers a significant advancement in automatic sub-cortical brain structure segmentation.
• The proposed DC-LSTM module is effective in enhancing feature representation and contextual understanding in deep networks.
• Ψ-Net shows strong potential for clinical applications in the diagnosis of neuropsychiatric disorders.