Evaluation of Extra Pixel Interpolation with Mask Processing for Medical Image Segmentation with Deep Learning
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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.
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
6 of 14 references.
References · 6
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2021Effects of Image Size on Deep Learningcited by 8
- 2020Software Implementation of Optimized Bicubic Interpolated Scan Conversion in Echocardiographycited by 5
- 2020Effect of the regularization hyperparameter on deep learning-based segmentation in LGE-MRIcited by 4
- 2020Evaluation of deep learning-based myocardial infarction quantification using Segment CMR softwarecited by 2
- 2021Stochastic Rounding for Image Interpolation and Scan Conversioncited by 2
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Citing papers per year: 2025: 1; 2026: 2.
3 papers.