Effective integration of object boundaries and regions for improving the performance of medical image segmentation by...

The existing CNN-based methods for object segmentation use the regions of objects alone as the labels for training networks, and the potentially useful boundaries annotated by radiologists are not used directly during the training. Thus, we proposed a framework of two cascaded networks to integrate both the region and boundary information for improving the accuracy of object segmentation.

The first network was used to extract the boundary from original images. The predicted dilated boundary from the first network and the corresponding original image were employed to train the second network for final segmentation. Compared with the object regions, the boundaries may provide additional useful local information for improved object segmentation. The two cascaded networks were evaluated on three datasets, including 40 CT scans for segmenting the esophagus, heart, trachea, and aorta, 247 chest radiographs for segmenting the lung, heart, and clavicle, and 101 retinal images for segmenting the optical disk and cup. The mean values of Dices, 90% Hausdorff distance, and Euclidean distance were employed to quantitatively evaluate the segmentation results.

Compared with the baseline method of the conventional U-Net, the two cascaded networks consistently improved the mean Dices and reduced the mean 90% Hausdorff distances and Euclidean distances for all objects, and the reduction rate of the 90% Hausdorff distance was as high as ten times for certain objects.

The boundary is very useful information for object segmentation, and the integration of object boundary and region would improve the segmentation results compared with the use of object region alone.