Topology-Aware Loss for Aorta and Great Vessel Segmentation in Computed Tomography Images

• URL: http://arxiv.org/abs/2307.03137v2
• Date: Sat, 24 Feb 2024 10:44:33 GMT
• Title: Topology-Aware Loss for Aorta and Great Vessel Segmentation in Computed Tomography Images
• Authors: Seher Ozcelik, Sinan Unver, Ilke Ali Gurses, Rustu Turkay, and Cigdem Gunduz-Demir
• Abstract summary: This paper introduces a new topology-aware loss function that penalizes topology dissimilarities between the ground truth and prediction. Our experiments on 4327 CT images of 24 subjects reveal that the proposed topology-aware loss function leads to better results than its counterparts.
• Score: 1.4680035572775534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Segmentation networks are not explicitly imposed to learn global invariants of an image, such as the shape of an object and the geometry between multiple objects, when they are trained with a standard loss function. On the other hand, incorporating such invariants into network training may help improve performance for various segmentation tasks when they are the intrinsic characteristics of the objects to be segmented. One example is segmentation of aorta and great vessels in computed tomography (CT) images where vessels are found in a particular geometry in the body due to the human anatomy and they mostly seem as round objects on a 2D CT image. This paper addresses this issue by introducing a new topology-aware loss function that penalizes topology dissimilarities between the ground truth and prediction through persistent homology. Different from the previously suggested segmentation network designs, which apply the threshold filtration on a likelihood function of the prediction map and the Betti numbers of the ground truth, this paper proposes to apply the Vietoris-Rips filtration to obtain persistence diagrams of both ground truth and prediction maps and calculate the dissimilarity with the Wasserstein distance between the corresponding persistence diagrams. The use of this filtration has advantage of modeling shape and geometry at the same time, which may not happen when the threshold filtration is applied. Our experiments on 4327 CT images of 24 subjects reveal that the proposed topology-aware loss function leads to better results than its counterparts, indicating the effectiveness of this use.

Related papers

• PI-Att: Topology Attention for Segmentation Networks through Adaptive Persistence Image Representation [1.4680035572775534]
  We introduce a new topology-aware loss function, which explicitly forces the network to minimize the topological dissimilarity between the ground truth and prediction maps. We quantify the topology of each map by the persistence image representation, for the first time in the context of a segmentation network loss. The effectiveness of the proposed PI-Att loss is demonstrated on two different datasets for aorta and great vessel segmentation in computed tomography images.
  arXiv  Detail & Related papers  (2024-08-15T09:06:49Z)
• Contour-weighted loss for class-imbalanced image segmentation [2.183832403223894]
  Image segmentation is critically important in almost all medical image analysis for automatic interpretations and processing. It is often challenging to perform image segmentation due to data imbalance between intra- and inter-class. We propose a new methodology to address the issue, with a compact yet effective contour-weighted loss function.
  arXiv  Detail & Related papers  (2024-06-07T07:43:52Z)
• Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation [133.02190910009384]
  We propose a two-stream graph convolutional network (i.e., TSGCN) to handle inter-view confusion between different raw attributes. Our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation.
  arXiv  Detail & Related papers  (2022-04-19T10:41:09Z)
• Topology-Preserving Segmentation Network: A Deep Learning Segmentation Framework for Connected Component [7.95119530218428]
  In medical imaging, the topology of the structure, such as the kidney or lung, is usually known. A it topology-preserving segmentation network (TPSN) is trained to give an accurate segmentation result. TPSN is a deformation-based model that yields a deformation map through a UNet. A multi-scale TPSN is developed in this paper that incorporates multi-level information of images to produce more precise segmentation results.
  arXiv  Detail & Related papers  (2022-02-27T09:56:33Z)
• Image Segmentation with Homotopy Warping [10.093435601073484]
  topological correctness is crucial for the segmentation of images with fine-scale structures. By leveraging the theory of digital topology, we identify locations in an image that are critical for topology. We propose a new homotopy warping loss to train deep image segmentation networks for better topological accuracy.
  arXiv  Detail & Related papers  (2021-12-15T00:33:15Z)
• 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)
• SOSD-Net: Joint Semantic Object Segmentation and Depth Estimation from Monocular images [94.36401543589523]
  We introduce the concept of semantic objectness to exploit the geometric relationship of these two tasks. We then propose a Semantic Object and Depth Estimation Network (SOSD-Net) based on the objectness assumption. To the best of our knowledge, SOSD-Net is the first network that exploits the geometry constraint for simultaneous monocular depth estimation and semantic segmentation.
  arXiv  Detail & Related papers  (2021-01-19T02:41:03Z)
• 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 Semantic Matching with Foreground Detection and Cycle-Consistency [103.22976097225457]
  We address weakly supervised semantic matching based on a deep network. We explicitly estimate the foreground regions to suppress the effect of background clutter. We develop cycle-consistent losses to enforce the predicted transformations across multiple images to be geometrically plausible and consistent.
  arXiv  Detail & Related papers  (2020-03-31T22:38:09Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)
• clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation [57.20783326661043]
  We introduce a novel similarity measure termed centerlineDice (short clDice) We theoretically prove that clDice guarantees topology preservation up to homotopy equivalence for binary 2D and 3D segmentation. We benchmark the soft-clDice loss on five public datasets, including vessels, roads and neurons (2D and 3D)
  arXiv  Detail & Related papers  (2020-03-16T16:27:49Z)

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.