mlsanityData to 14 Oct 2026

Cutting-Splicing data augmentation: A novel technology for medical image segmentation

arXiv 2210.09099 ↗First posted 17 Oct 2022cs.CVcited by 0

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Abstract (arXiv)

Background: Medical images are more difficult to acquire and annotate than natural images, which results in data augmentation technologies often being used in medical image segmentation tasks. Most data augmentation technologies used in medical segmentation were originally developed on natural images and do not take into account the characteristic that the overall layout of medical images is standard and fixed. Methods: Based on the characteristics of medical images, we developed the cutting-splicing data augmentation (CS-DA) method, a novel data augmentation technology for medical image segmentation. CS-DA augments the dataset by splicing different position components cut from different original medical images into a new image. The characteristics of the medical image result in the new image having the same layout as and similar appearance to the original image. Compared with classical data augmentation technologies, CS-DA is simpler and more robust. Moreover, CS-DA does not introduce any noise or fake information into the newly created image. Results: To explore the properties of CS-DA, many experiments are conducted on eight diverse datasets. On the training dataset with the small sample size, CS-DA can effectively increase the performance of the segmentation model. When CS-DA is used together with classical data augmentation technologies, the performance of the segmentation model can be further improved and is much better than that of CS-DA and classical data augmentation separately. We also explored the influence of the number of components, the position of the cutting line, and the splicing method on the CS-DA performance. Conclusions: The excellent performance of CS-DA in the experiment has confirmed the effectiveness of CS-DA, and provides a new data augmentation idea for the small sample segmentation task.

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References · 14

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  3. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,476
  4. 2017Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentationscited by 620
  5. 2018Rotation Equivariant CNNs for Digital Pathologycited by 422
  6. 2019Deep Learning for Anomaly Detection: A Surveycited by 358
  7. 2020CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentationcited by 309
  8. 2018GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classificationcited by 242
  9. 2018Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networkscited by 147
  10. 2019Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentationcited by 140
  11. 2015Brain Tumor Segmentation with Deep Neural Networkscited by 137
  12. 2018DeepPET: A deep encoder-decoder network for directly solving the PET reconstruction inverse problemcited by 40
2 more
  1. 2018An application of cascaded 3D fully convolutional networks for medical image segmentationcited by 34
  2. 2020Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problemcited by 9

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