Dilated residual FPN-based segmentation for mouse retinal images

Diabetes can induce diabetic retinopathy (DR), and the blindness caused by this disease is irreversible. The early analysis of mouse retinal images, including the layer and cell segmentation properties of these images, can help to effectively diagnose this disease.

In the study, we design a dilated residual method based on a feature pyramid network (FPN), in which the FPN is adopted as the base network for solving the multiscale segmentation problem concerning mouse retinal images. In the bottom-up encoding pathway, we construct our backbone feature extraction network via the combination of dilated convolution and a residual block, further increasing the range of the receptive field to obtain more context information. At the same time, we integrate a squeeze-and-excitation (SE) attention module into the backbone network to obtain more small object details. In the top-down decoding pathway, we replace the traditional nearest-neighbor upsampling method with the transposed convolution method and add a segmentation head to obtain semantic segmentation results.

The effectiveness of our network model is verified in two segmentation tasks: ganglion cell segmentation and mouse retinal cell and layer segmentation. The outcomes demonstrate that, compared to other supervised segmentation methods based on deep learning, our model attains the utmost precision in both binary segmentation and multiclass semantic segmentation tasks.

The dilated residual FPN is a robust method for mouse retinal image segmentation and it can effectively assist DR diagnosis.