A gentle introduction to deep learning in medical image processing

Image detection and recognition

Image detection and recognition deals with the problem of detecting a certain element in a medical image. In many cases, the images are volumetric. Therefore efficient parsing is a must. A popular strategy to do so is marginal space learn- ing [64], as it is efficient and allows to detect organs robustly. Its deep learning counter-part [65] is even more efficient, as its probabilistic boosting trees are replaced using a neural network-based boosting cascade. Still, the entire volume has to be processed to detect anatomical structures reliably. [65] drives efficiency even further by replacing the search process by an artificial agent that follows anatomy to detect anatomical landmarks using deep reinforcement learning. The method is able to detect hundreds of landmarks in a complete CT volume in few seconds.

Bier et al. proposed an interesting method in which they detect anatomical landmarks in 2-D X-ray projection images [66]. In their method, they train projection-invariant fea- ture descriptors from 3-D annotated landmarks using a deep network. Yet another popular method for detection are the so- called region proposal convolutional neural networks. In [67] they are applied to robustly detect tumors in mammographic images.
Detection and recognition are obviously also applied in many other modalities and a great body of literature exists. Here, we only report two more applications. In histology, cell detection and classification is an important task, which is tackled by Aubreville et al. using guided spatial transformer networks [68] that allow refinement of the detection before the actual classification is done. The task of mitosis classification benefits from this procedure. Convolutional neural networks are also very effective for other image classification tasks. In
[69] they are employed to automatically detect images con- taining motion artifacts in confocal laser-endoscopy images.

2.