Despite the recent success of deep learning methods at achieving new
state-of-the-art accuracy for medical image segmentation, some major
limitations are still restricting their deployment into clinics. One major
limitation of deep learning-based segmentation methods is their lack of
robustness to variability in the image acquisition protocol and in the imaged
anatomy that were not represented or were underrepresented in the training
dataset. This suggests adding new manually segmented images to the training
dataset to better cover the image variability. However, in most cases, the
manual segmentation of medical images requires highly skilled raters and is
time-consuming, making this solution prohibitively expensive. Even when
manually segmented images from different sources are available, they are rarely
annotated for exactly the same regions of interest. This poses an additional
challenge for current state-of-the-art deep learning segmentation methods that
rely on supervised learning and therefore require all the regions of interest
to be segmented for all the images to be used for training. This thesis
introduces new mathematical and optimization methods to mitigate those
limitations.

该论文研究利用新的数学和优化方法来解决基于深度学习的医学图像分割方法在图像获取协议和图像解剖变异性方面的限制，以减少需要手动标记成本高昂的问题。