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

03/31/2020
by   Dwarikanath Mahapatra, et al.
0

Medical image segmentation is an important task for computer aided diagnosis. Pixelwise manual annotations of large datasets require high expertise and is time consuming. Conventional data augmentations have limited benefit by not fully representing the underlying distribution of the training set, thus affecting model robustness when tested on images captured from different sources. Prior work leverages synthetic images for data augmentation ignoring the interleaved geometric relationship between different anatomical labels. We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. Latent space variable sampling results in diverse generated images from a base image and improves robustness. Given those augmented images generated by our method, we train the segmentation network to enhance the segmentation performance of retinal optical coherence tomography (OCT) images. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures. Ablation studies and visual analysis also demonstrate benefits of integrating geometry and diversity.

READ FULL TEXT

page 1

page 2

page 4

page 7

page 8

research
06/15/2021

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

While medical image segmentation is an important task for computer aided...
research
10/01/2021

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...
research
01/31/2020

Inter-slice image augmentation based on frame interpolation for boosting medical image segmentation accuracy

We introduce the idea of inter-slice image augmentation whereby the numb...
research
09/02/2023

AdLER: Adversarial Training with Label Error Rectification for One-Shot Medical Image Segmentation

Accurate automatic segmentation of medical images typically requires lar...
research
09/10/2020

Assignment Flow for Order-Constrained OCT Segmentation

At the present time Optical Coherence Tomography (OCT) is among the most...
research
04/21/2020

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

In medical image diagnosis, pathology image analysis using semantic segm...
research
12/28/2020

Analysis of Macula on Color Fundus Images Using Heightmap Reconstruction Through Deep Learning

For medical diagnosis based on retinal images, a clear understanding of ...

Please sign up or login with your details

Forgot password? Click here to reset