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The Canal-Net architecture with a 3D encoder-decoder under a multi-task learning framework consisting of time-distributed convolution blocks, multi-scale inputs, skip connection, and bidirectional convolutional LSTM (ConvLSTM) with side-output layers. The bidirectional ConvLSTM was utilized to capture anatomical context information, and a multi-task learning approach was performed to learn overall MC volume and structural continuity.
