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

by   Dwarikanath Mahapatra, et al.

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.


page 1

page 3

page 4

page 7

page 8

page 9


Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation

Medical image segmentation is an important task for computer aided diagn...

Learning of Inter-Label Geometric Relationships Using Self-Supervised Learning: Application To Gleason Grade Segmentation

Segmentation of Prostate Cancer (PCa) tissues from Gleason graded histop...

Synthetic Augmentation pix2pix using Tri-category Label with Edge structure for Accurate Segmentation architectures

In medical image diagnosis, pathology image analysis using semantic segm...

Conditional Diffusion Models for Weakly Supervised Medical Image Segmentation

Recent advances in denoising diffusion probabilistic models have shown g...

Less is More: Unsupervised Mask-guided Annotated CT Image Synthesis with Minimum Manual Segmentations

As a pragmatic data augmentation tool, data synthesis has generally retu...

Attention-based Dynamic Subspace Learners for Medical Image Analysis

Learning similarity is a key aspect in medical image analysis, particula...

Weakly-Supervised 3D Medical Image Segmentation using Geometric Prior and Contrastive Similarity

Medical image segmentation is almost the most important pre-processing p...

Please sign up or login with your details

Forgot password? Click here to reset