  Small liver lesions common to colorectal liver metastases (CRLMs) are
challenging for convolutional neural network (CNN) segmentation models,
especially when we have a wide range of slice thicknesses in the computed
tomography (CT) scans. Slice thickness of CT images may vary by clinical
indication. For example, thinner slices are used for presurgical planning when
fine anatomic details of small vessels are required. While keeping the
effective radiation dose in patients as low as possible, various slice
thicknesses are employed in CRLMs due to their limitations. However,
differences in slice thickness across CTs lead to significant performance
degradation in CT segmentation models based on CNNs. This paper proposes a
novel unsupervised attention-based interpolation model to generate intermediate
slices from consecutive triplet slices in CT scans. We integrate segmentation
loss during the interpolation model's training to leverage segmentation labels
in existing slices to generate middle ones. Unlike common interpolation
techniques in CT volumes, our model highlights the regions of interest (liver
and lesions) inside the abdominal CT scans in the interpolated slice. Moreover,
our model's outputs are consistent with the original input slices while
increasing the segmentation performance in two cutting-edge 3D segmentation
pipelines. We tested the proposed model on the CRLM dataset to upsample
subjects with thick slices and create isotropic volume for our segmentation
model. The produced isotropic dataset increases the Dice score in the
segmentation of lesions and outperforms other interpolation approaches in terms
of interpolation metrics.
