Promises and limitations of deep learning for medical image segmentation

Christian S. Perone, Julien Cohen-Adad


It is not a secret that recent advances in deep learning (1) methods have achieved a scientific and engineering milestone in many different fields such as natural language processing, computer vision, speech recognition, object detection, and segmentation, to name a few. Different applications of deep learning to medical imaging started to appear first in workshops, conferences and then in journals. According to a recent survey (2), the number of papers grew rapidly in 2015 and 2016. Nowadays, deep learning methods are pervasive throughout the entire medical imaging community, with Convolutional Neural Networks (CNNs) being the most used model for tasks such as dense prediction (or segmentation), detection and classification. In the same survey, which analyzed more than 300 contributions in the field, the authors found that computed tomography (CT) was the third most used imaging modality.

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