# Evaluation of Extra Pixel Interpolation with Mask Processing for Medical Image Segmentation with Deep Learning

arXiv: https://arxiv.org/abs/2302.11522 · HTML: https://mlsanity.com/paper/2302.11522/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 22 Feb 2023.

## Builds on (most cited first, 6 of 6)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Effects of Image Size on Deep Learning](https://mlsanity.com/paper/2101.11508/) (2021)
- [Software Implementation of Optimized Bicubic Interpolated Scan Conversion in Echocardiography](https://mlsanity.com/paper/2005.11269/) (2020)
- [Effect of the regularization hyperparameter on deep learning-based segmentation in LGE-MRI](https://mlsanity.com/paper/2012.05661/) (2020)
- [Evaluation of deep learning-based myocardial infarction quantification using Segment CMR software](https://mlsanity.com/paper/2012.09070/) (2020)
- [Stochastic Rounding for Image Interpolation and Scan Conversion](https://mlsanity.com/paper/2110.12983/) (2021)

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

- [Beyond Nearest Neighbor Interpolation in Data Augmentation](https://mlsanity.com/paper/2504.01527/) (2025)
- [Temporally Consistent Label Interpolation for Robust Surgical Multi-Task Learning under Challenging Conditions](https://mlsanity.com/paper/2606.26634/) (2026)
- [Stride Independent Patching for Deep Learning](https://mlsanity.com/paper/2610.12216/) (2026)

## Abstract (arXiv)

Current mask processing operations rely on interpolation algorithms that do not produce extra pixels, such as nearest neighbor (NN) interpolation, as opposed to algorithms that do produce extra pixels, like bicubic (BIC) or bilinear (BIL) interpolation. In our previous study, the author proposed an alternative approach to NN-based mask processing and evaluated its effects on deep learning training outcomes. In this study, the author evaluated the effects of both BIC-based image and mask processing and BIC-and-NN-based image and mask processing versus NN-based image and mask processing. The evaluation revealed that the BIC-BIC model/network was an 8.9578 % (with image size 256 x 256) and a 1.0496 % (with image size 384 x 384) increase of the NN-NN network compared to the NN-BIC network which was an 8.3127 % (with image size 256 x 256) and a 0.2887 % (with image size 384 x 384) increase of the NN-NN network.