Deep learning methods to aid lesion analysis in medical images

In this thesis, novel deep learning-based methods and extensions to existing methods
that aid the segmentation and analysis of lesions have been presented. In the first half
of this thesis (Chapter 2 and Chapter 3), we studied the role of uncertainty estimation
methods in improving the performance of deep learning-based lesion segmentation
models. Our work on false-positive reduction (Chapter 2) showed that the miscalibration
present in the predictions prevents a meaningful interpretation of uncertainty estimates.
We demonstrated the benefits of using more expressive distributions to model the
latent space distribution to handle the segmentation of ambiguous images (Chapter
3). In the second half of the thesis (Chapter 4 and Chapter 5), we showed that
the use of learned landmark correspondences can improve the registration of organs
that exhibit a high degree of motion or deformation, with a focus on the clinical
task of lesion co-localization. We extended an existing deep learning-based landmark
correspondence prediction model, DCNN-Match, by introducing a soft mask loss term
and demonstrated an improvement in lung CT registration in the absence of lung masks
(Chapter 4). We also showed that using learned landmark correspondences, on or near
vessels in the liver, can improve lesion co-localization over standard intensity-based
image registration (Chapter 5).