mlsanityData to 14 Oct 2026

CT Image Synthesis Using Weakly Supervised Segmentation and Geometric Inter-Label Relations For COVID Image Analysis

arXiv 2106.10230 ↗First posted 15 Jun 2021eess.IV cs.CVcited by 8

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Abstract (arXiv)

While medical image segmentation is an important task for computer aided diagnosis, the high expertise requirement for pixelwise manual annotations makes it a challenging and time consuming task. Since conventional data augmentations do not fully represent the underlying distribution of the training set, the trained models have varying performance when tested on images captured from different sources. Most prior work on image synthesis for data augmentation ignore the interleaved geometric relationship between different anatomical labels. We propose improvements over previous GAN-based medical image synthesis methods by learning the relationship between different anatomical labels. We use a weakly supervised segmentation method to obtain pixel level semantic label map of images which is used learn the intrinsic relationship of geometry and shape across semantic labels. Latent space variable sampling results in diverse generated images from a base image and improves robustness. We use the synthetic images from our method to train networks for segmenting COVID-19 infected areas from lung CT images. The proposed method outperforms state-of-the-art segmentation methods on a public dataset. Ablation studies also demonstrate benefits of integrating geometry and diversity.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 46 of its 156 references that are in the map. First posted on arXiv 15 Jun 2021.

Builds on

46 of 156 references.

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34 more
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Cited by year

Citing papers per year: 2021: 2; 2022: 4; 2023: 1; 2024: 0; 2025: 1.

8 papers.

  1. 2021Learning of Inter-Label Geometric Relationships Using Self-Supervised Learning: Application To Gleason Grade Segmentationcited by 5
  2. 2022Unsupervised Domain Adaptation Using Feature Disentanglement And GCNs For Medical Image Classificationcited by 5
  3. 2021Multimodal Generalized Zero Shot Learning for Gleason Grading using Self-Supervised Learningcited by 4
  4. 2023DACov: A Deeper Analysis of Data Augmentation on the Computed Tomography Segmentation Problemcited by 3
  5. 2022Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classificationcited by 2
  6. 2022Improved Super Resolution of MR Images Using CNNs and Vision Transformerscited by 1
  7. 2022Cyclic Generative Adversarial Networks With Congruent Image-Report Generation For Explainable Medical Image Analysiscited by 0
  8. 2025Sound Signal Synthesis with Auxiliary Classifier GAN, COVID-19 cough as an examplecited by 0