Automatic segmentation of medical images is an important task for many
clinical applications. In practice, a wide range of anatomical structures are
visualised using different imaging modalities. In this paper, we investigate
whether a single convolutional neural network (CNN) can be trained to perform
different segmentation tasks.
A single CNN is trained to segment six tissues in MR brain images, the
pectoral muscle in MR breast images, and the coronary arteries in cardiac CTA.
The CNN therefore learns to identify the imaging modality, the visualised
anatomical structures, and the tissue classes.
For each of the three tasks (brain MRI, breast MRI and cardiac CTA), this
combined training procedure resulted in a segmentation performance equivalent
to that of a CNN trained specifically for that task, demonstrating the high
capacity of CNN architectures. Hence, a single system could be used in clinical
practice to automatically perform diverse segmentation tasks without
task-specific training.

本文研究使用单个卷积神经网络对不同成像模式下的医学图像进行分割的方法，实验结果表明这种方法在不同任务中具有相当的分割性能，证明了卷积神经网络结构的高容量性。因此，可以在医学实践中使用单个系统进行自动分割任务，而无需进行任务特定的训练。