  Due to the irregular motion, similar appearance and diverse shape, accurate
segmentation of kidney tumor in CT images is a difficult and challenging task.
To this end, we present a novel automatic segmentation method, termed as
Crossbar-Net, with the goal of accurate segmenting the kidney tumors. Firstly,
considering that the traditional learning-based segmentation methods normally
employ either whole images or squared patches as the training samples, we
innovatively sample the orthogonal non-squared patches (namely crossbar
patches), to fully cover the whole kidney tumors in either horizontal or
vertical directions. These sampled crossbar patches could not only represent
the detailed local information of kidney tumor as the traditional patches, but
also describe the global appearance from either horizontal or vertical
direction using contextual information. Secondly, with the obtained crossbar
patches, we trained a convolutional neural network with two sub-models (i.e.,
horizontal sub-model and vertical sub-model) in a cascaded manner, to integrate
the segmentation results from two directions (i.e., horizontal and vertical).
This cascaded training strategy could effectively guarantee the consistency
between sub-models, by feeding each other with the most difficult samples, for
a better segmentation. In the experiment, we evaluate our method on a real CT
kidney tumor dataset, collected from 94 different patients including 3,500
images. Compared with the state-of-the-art segmentation methods, the results
demonstrate the superior results of our method on dice ratio score, true
positive fraction, centroid distance and Hausdorff distance. Moreover, we have
extended our crossbar-net to a different task: cardiac segmentation, showing
the promising results for the better generalization.
