Offset Curves Loss for Imbalanced Problem in Medical Segmentation

• URL: http://arxiv.org/abs/2012.02463v1
• Date: Fri, 4 Dec 2020 08:35:21 GMT
• Title: Offset Curves Loss for Imbalanced Problem in Medical Segmentation
• Authors: Ngan Le, Trung Le, Kashu Yamazaki, Toan Duc Bui, Khoa Luu, Marios Savides
• Abstract summary: We develop a new deep learning-based model which takes into account both higher feature level i.e. region inside contour and lower feature level i.e. contour. Our proposed Offset Curves (OsC) loss consists of three main fitting terms. We evaluate our proposed OsC loss on both 2D network and 3D network.
• Score: 15.663236378920637
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Class-Imbalanced Semi-Supervised Learning for Large-Scale Point Cloud Semantic Segmentation via Decoupling Optimization [64.36097398869774]
  Semi-supervised learning (SSL) has been an active research topic for large-scale 3D scene understanding. The existing SSL-based methods suffer from severe training bias due to class imbalance and long-tail distributions of the point cloud data. We introduce a new decoupling optimization framework, which disentangles feature representation learning and classifier in an alternative optimization manner to shift the bias decision boundary effectively.
  arXiv  Detail & Related papers  (2024-01-13T04:16:40Z)
• Semi-Supervised Hybrid Spine Network for Segmentation of Spine MR Images [14.190504802866288]
  We propose a two-stage algorithm, named semi-supervised hybrid spine network (SSHSNet) to achieve simultaneous vertebral bodies (VBs) and intervertebral discs (IVDs) segmentation. In the first stage, we constructed a 2D semi-supervised DeepLabv3+ by using cross pseudo supervision to obtain intra-slice features and coarse segmentation. In the second stage, a 3D full-resolution patch-based DeepLabv3+ was built to extract inter-slice information. Results show that the proposed method has great potential in dealing with the data imbalance problem
  arXiv  Detail & Related papers  (2022-03-23T02:57:14Z)
• Cross-Site Severity Assessment of COVID-19 from CT Images via Domain Adaptation [64.59521853145368]
  Early and accurate severity assessment of Coronavirus disease 2019 (COVID-19) based on computed tomography (CT) images offers a great help to the estimation of intensive care unit event. To augment the labeled data and improve the generalization ability of the classification model, it is necessary to aggregate data from multiple sites. This task faces several challenges including class imbalance between mild and severe infections, domain distribution discrepancy between sites, and presence of heterogeneous features.
  arXiv  Detail & Related papers  (2021-09-08T07:56:51Z)
• A persistent homology-based topological loss for CNN-based multi-class segmentation of CMR [5.898114915426535]
  Multi-class segmentation of cardiac magnetic resonance (CMR) images seeks a separation of data into anatomical components with known structure and configuration. Most popular CNN-based methods are optimised using pixel wise loss functions, ignorant of the spatially extended features that characterise anatomy. We extend these approaches to the task of multi-class segmentation by building an enriched topological description of all class labels and class label pairs.
  arXiv  Detail & Related papers  (2021-07-27T09:21:38Z)
• Residual Moment Loss for Medical Image Segmentation [56.72261489147506]
  Location information is proven to benefit the deep learning models on capturing the manifold structure of target objects. Most existing methods encode the location information in an implicit way, for the network to learn. We propose a novel loss function, namely residual moment (RM) loss, to explicitly embed the location information of segmentation targets.
  arXiv  Detail & Related papers  (2021-06-27T09:31:49Z)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)
• Learning Euler's Elastica Model for Medical Image Segmentation [28.638720771555914]
  We propose a novel active contour with elastica (ACE) loss function incorporating Elastica (curvature and length) and region information as geometrically-natural constraints for the image segmentation tasks. Our results show that the proposed loss function outperforms other mainstream loss functions on different segmentation networks.
  arXiv  Detail & Related papers  (2020-11-01T15:14:37Z)
• Context-Aware Refinement Network Incorporating Structural Connectivity Prior for Brain Midline Delineation [50.868845400939314]
  We propose a context-aware refinement network (CAR-Net) to refine and integrate the feature pyramid representation generated by the UNet. For keeping the structural connectivity of the brain midline, we introduce a novel connectivity regular loss. The proposed method requires fewer parameters and outperforms three state-of-the-art methods in terms of four evaluation metrics.
  arXiv  Detail & Related papers  (2020-07-10T14:01:20Z)
• An Elastic Interaction-Based Loss Function for Medical Image Segmentation [10.851295591782538]
  This paper introduces a long-range elastic interaction-based training strategy for medical image segmentation. In this strategy, CNN learns the target region under the guidance of the elastic interaction energy between the boundary of the predicted region and that of the actual object. Experimental results show that our method is able to achieve considerable improvements compared to commonly used pixel-wise loss functions.
  arXiv  Detail & Related papers  (2020-07-06T11:49:14Z)
• Deep Q-Network-Driven Catheter Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning and Dual-UNet [74.22397862400177]
  We propose a novel catheter segmentation approach, which requests fewer annotations than the supervised learning method. Our scheme considers a deep Q learning as the pre-localization step, which avoids voxel-level annotation. With the detected catheter, patch-based Dual-UNet is applied to segment the catheter in 3D volumetric data.
  arXiv  Detail & Related papers  (2020-06-25T21:10:04Z)
• DSU-net: Dense SegU-net for automatic head-and-neck tumor segmentation in MR images [30.747375849126925]
  We propose a Dense SegU-net (DSU-net) framework for automatic nasopharyngeal carcinoma (NPC) segmentation in MRI. To combat the potential vanishing-gradient problem, we introduce dense blocks which can facilitate feature propagation and reuse. Our proposed architecture outperforms the existing state-of-the-art segmentation networks.
  arXiv  Detail & Related papers  (2020-06-11T09:33:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.