DeepAI
Log In Sign Up

Offset Curves Loss for Imbalanced Problem in Medical Segmentation

12/04/2020
by   Ngan Le, et al.
20

Medical image segmentation has played an important role in medical analysis and widely developed for many clinical applications. Deep learning-based approaches have achieved high performance in semantic segmentation but they are limited to pixel-wise setting and imbalanced classes data problem. In this paper, we tackle those limitations by developing a new deep learning-based model which takes into account both higher feature level i.e. region inside contour, intermediate feature level i.e. offset curves around the contour and lower feature level i.e. contour. Our proposed Offset Curves (OsC) loss consists of three main fitting terms. The first fitting term focuses on pixel-wise level segmentation whereas the second fitting term acts as attention model which pays attention to the area around the boundaries (offset curves). The third terms plays a role as regularization term which takes the length of boundaries into account. We evaluate our proposed OsC loss on both 2D network and 3D network. Two common medical datasets, i.e. retina DRIVE and brain tumor BRATS 2018 datasets are used to benchmark our proposed loss performance. The experiments have shown that our proposed OsC loss function outperforms other mainstream loss functions such as Cross-Entropy, Dice, Focal on the most common segmentation networks Unet, FCN.

READ FULL TEXT

page 2

page 4

page 5

page 6

08/10/2022

KiPA22 Report: U-Net with Contour Regularization for Renal Structures Segmentation

Three-dimensional (3D) integrated renal structures (IRS) segmentation is...
08/02/2021

BezierSeg: Parametric Shape Representation for Fast Object Segmentation in Medical Images

Delineating the lesion area is an important task in image-based diagnosi...
11/01/2020

Learning Euler's Elastica Model for Medical Image Segmentation

Image segmentation is a fundamental topic in image processing and has be...
07/06/2020

An Elastic Interaction-Based Loss Function for Medical Image Segmentation

Deep learning techniques have shown their success in medical image segme...
05/21/2019

Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation

Semantic segmentation is essentially important to biomedical image analy...
02/03/2020

Bending Loss Regularized Network for Nuclei Segmentation in Histopathology Images

Separating overlapped nuclei is a major challenge in histopathology imag...
12/17/2018

Boundary loss for highly unbalanced segmentation

Widely used loss functions for convolutional neural network (CNN) segmen...