  The domain gap caused mainly by variable medical image quality renders a
major obstacle on the path between training a segmentation model in the lab and
applying the trained model to unseen clinical data. To address this issue,
domain generalization methods have been proposed, which however usually use
static convolutions and are less flexible. In this paper, we propose a
multi-source domain generalization model based on the domain and content
adaptive convolution (DCAC) for the segmentation of medical images across
different modalities. Specifically, we design the domain adaptive convolution
(DAC) module and content adaptive convolution (CAC) module and incorporate both
into an encoder-decoder backbone. In the DAC module, a dynamic convolutional
head is conditioned on the predicted domain code of the input to make our model
adapt to the unseen target domain. In the CAC module, a dynamic convolutional
head is conditioned on the global image features to make our model adapt to the
test image. We evaluated the DCAC model against the baseline and four
state-of-the-art domain generalization methods on the prostate segmentation,
COVID-19 lesion segmentation, and optic cup/optic disc segmentation tasks. Our
results not only indicate that the proposed DCAC model outperforms all
competing methods on each segmentation task but also demonstrate the
effectiveness of the DAC and CAC modules. Code is available at
\url{https://git.io/DCAC}.
