I have changed the initialization of Pytorch and Keras to “zeros” for weights and bias and it worked. Thank you very much for your help @ptrblck !!!
[Keras]
seq.add(Conv3D(filters=1, kernel_size=(3,3,3), padding='same', 
				data_format='channels_last', 
                kernel_initializer='zeros', 
				bias_initializer='zeros'))

[PyTorch]
        self.conv = nn.Conv3d(in_channels=5, out_channels=1, kernel_size=(3,3,3), 
padding=(1,1,1))
        nn.init.zeros_(self.conv.weight)
        nn.init.zeros_(self.conv.bias)

Results
[PyTorch] and [Keras]
Train on 80 samples, validate on 20 samples
Epoch: 1/10 - loss: 8.7088 - val_loss: 4.0080
Epoch: 2/10 - loss: 3.6342 - val_loss: 3.9557
Epoch: 3/10 - loss: 3.5624 - val_loss: 3.8861
(...)
Epoch: 8/10 - loss: 3.2063 - val_loss: 3.5333
Epoch: 9/10 - loss: 3.1367 - val_loss: 3.4623
Epoch: 10/10 - loss: 3.0679 - val_loss: 3.3903

Test_loss: 3.08

Just for the records, I tried first to use glorot/xavier_uniform for weights, but it seems that the implementations of Pytorch and Keras are slightly diffent. Because in Keras you can set a seed or if you don’t the default value used is seed = np.random.randint(10e6). With this initialization method, Pytorch and Keras gave different results. So, that’s why I changed to zeros.