Table 1 Algorithm Description of DeepHipp

Input: Training data with augmentation: \(\chi\)

1: Initialization: learning rate=0.01, bach_size=16, kernel size=3, randomly initialize W, b;

2: for \(epoch=1\); \(epoch \le {epoch}_{max}\); \(epoch++ do\);

3:        Compute \(Dice\left( {y_{i},~\overset{\sim }{y_{i}}} \right)\);

4:        Compute \(Loss = 1 - Dice\left( {y_{i},~\overset{\sim }{y_{i}}} \right)\);

5:        Compute \(Minimum\left( {Loss} \right) ;\)

6:    Update the model parameter W, b based on \(\chi\);

7: end for;

Output: The segmentation results from Model