  Segmentation of enhancing tumours or lesions from MRI is important for
detecting new disease activity in many clinical contexts. However, accurate
segmentation requires the inclusion of medical images (e.g., T1 post contrast
MRI) acquired after injecting patients with a contrast agent (e.g.,
Gadolinium), a process no longer thought to be safe. Although a number of
modality-agnostic segmentation networks have been developed over the past few
years, they have been met with limited success in the context of enhancing
pathology segmentation. In this work, we present HAD-Net, a novel offline
adversarial knowledge distillation (KD) technique, whereby a pre-trained
teacher segmentation network, with access to all MRI sequences, teaches a
student network, via hierarchical adversarial training, to better overcome the
large domain shift presented when crucial images are absent during inference.
In particular, we apply HAD-Net to the challenging task of enhancing tumour
segmentation when access to post-contrast imaging is not available. The
proposed network is trained and tested on the BraTS 2019 brain tumour
segmentation challenge dataset, where it achieves performance improvements in
the ranges of 16% - 26% over (a) recent modality-agnostic segmentation methods
(U-HeMIS, U-HVED), (b) KD-Net adapted to this problem, (c) the pre-trained
student network and (d) a non-hierarchical version of the network (AD-Net), in
terms of Dice scores for enhancing tumour (ET). The network also shows
improvements in tumour core (TC) Dice scores. Finally, the network outperforms
both the baseline student network and AD-Net in terms of uncertainty
quantification for enhancing tumour segmentation based on the BraTs 2019
uncertainty challenge metrics. Our code is publicly available at:
https://github.com/SaverioVad/HAD_Net
