# Attention-based CT Scan Interpolation for Lesion Segmentation of Colorectal Liver Metastases

arXiv: https://arxiv.org/abs/2308.15932 · HTML: https://mlsanity.com/paper/2308.15932/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 27 references that are in the map.
- First posted on arXiv 30 Aug 2023.

## Builds on (most cited first, 15 of 15)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [Conditional Image Generation with PixelCNN Decoders](https://mlsanity.com/paper/1606.05328/) (2016)
- [The Medical Segmentation Decathlon](https://mlsanity.com/paper/2106.05735/) (2021)
- [The Liver Tumor Segmentation Benchmark (LiTS)](https://mlsanity.com/paper/1901.04056/) (2019)
- [Deep multi-scale video prediction beyond mean square error](https://mlsanity.com/paper/1511.05440/) (2015)
- [Video Frame Synthesis using Deep Voxel Flow](https://mlsanity.com/paper/1702.02463/) (2017)
- [Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Network](https://mlsanity.com/paper/1803.01417/) (2018)
- [Unsupervised Video Interpolation Using Cycle Consistency](https://mlsanity.com/paper/1906.05928/) (2019)
- [Explorable Super Resolution](https://mlsanity.com/paper/1912.01839/) (2019)
- [Modality Completion via Gaussian Process Prior Variational Autoencoders for Multi-Modal Glioma Segmentation](https://mlsanity.com/paper/2107.03442/) (2021)
- [High Tissue Contrast MRI Synthesis Using Multi-Stage Attention-GAN for Glioma Segmentation](https://mlsanity.com/paper/2006.05030/) (2020)
- [Convolutional 3D to 2D Patch Conversion for Pixel-wise Glioma Segmentation in MRI Scans](https://mlsanity.com/paper/2010.10612/) (2020)
- [Brain Tumor Synthetic Segmentation in 3D Multimodal MRI Scans](https://mlsanity.com/paper/1909.13640/) (2019)

## Built on by (most cited first, 3 of 3)

- [Finding Reproducible and Prognostic Radiomic Features in Variable Slice Thickness Contrast Enhanced CT of Colorectal Liver Metastases](https://mlsanity.com/paper/2501.11221/) (2025)
- [A Novel Patch-Based TDA Approach for Computed Tomography Imaging](https://mlsanity.com/paper/2512.12108/) (2025)
- [Parameter-Efficient Fine-Tuning of Foundation Models for Liver Tumor Segmentation in CT](https://mlsanity.com/paper/2609.14106/) (2026)

## Abstract (arXiv)

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.