Deep Learning Framework with Multi-Head Dilated Encoders for Enhanced
  Segmentation of Cervical Cancer on Multiparametric Magnetic Resonance Imaging
Reza Kalantar Sebastian Curcean Jessica M Winfield Gigin Lin Christina Messiou Matthew D Blackledge Dow-Mu Koh
T2-weighted magnetic resonance imaging (MRI) and diffusion-weighted imaging
(DWI) are essential components for cervical cancer diagnosis. However,
combining these channels for training deep learning models are challenging due
to misalignment of images. Here, we propose a novel multi-head framework that
uses dilated convolutions and shared residual connections for separate encoding
of multiparametric MRI images. We employ a residual U-Net model as a baseline,
and perform a series of architectural experiments to evaluate the tumor
segmentation performance based on multiparametric input channels and feature
encoding configurations. All experiments were performed using a cohort
including 207 patients with locally advanced cervical cancer. Our proposed
multi-head model using separate dilated encoding for T2W MRI, and combined
b1000 DWI and apparent diffusion coefficient (ADC) images achieved the best
median Dice coefficient similarity (DSC) score, 0.823 (95% confidence interval
(CI), 0.595-0.797), outperforming the conventional multi-channel model, DSC
0.788 (95% CI, 0.568-0.776), although the difference was not statistically
significant (p>0.05). We investigated channel sensitivity using 3D GRAD-CAM and
channel dropout, and highlighted the critical importance of T2W and ADC
channels for accurate tumor segmentations. However, our results showed that
b1000 DWI had a minor impact on overall segmentation performance. We
demonstrated that the use of separate dilated feature extractors and
independent contextual learning improved the model's ability to reduce the
boundary effects and distortion of DWI, leading to improved segmentation
performance. Our findings can have significant implications for the development
of robust and generalizable models that can extend to other multi-modal
segmentation applications.