Skip priors and add graph-based anatomical information, for point-based Couinaud segmentation
- URL: http://arxiv.org/abs/2508.01785v1
- Date: Sun, 03 Aug 2025 14:52:14 GMT
- Title: Skip priors and add graph-based anatomical information, for point-based Couinaud segmentation
- Authors: Xiaotong Zhang, Alexander Broersen, Gonnie CM van Erp, Silvia L. Pintea, Jouke Dijkstra,
- Abstract summary: preoperative planning of liver surgery relies on Couinaud segmentation from computed tomography (CT) images.<n>Using 3D point-based representations, rather than voxelizing the CT volume, has the benefit of preserving the physical resolution of the CT.<n>Here, we propose a point-based method for Couinaud segmentation, without explicitly providing the prior liver vessel structure.
- Score: 44.706905779969404
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The preoperative planning of liver surgery relies on Couinaud segmentation from computed tomography (CT) images, to reduce the risk of bleeding and guide the resection procedure. Using 3D point-based representations, rather than voxelizing the CT volume, has the benefit of preserving the physical resolution of the CT. However, point-based representations need prior knowledge of the liver vessel structure, which is time consuming to acquire. Here, we propose a point-based method for Couinaud segmentation, without explicitly providing the prior liver vessel structure. To allow the model to learn this anatomical liver vessel structure, we add a graph reasoning module on top of the point features. This adds implicit anatomical information to the model, by learning affinities across point neighborhoods. Our method is competitive on the MSD and LiTS public datasets in Dice coefficient and average surface distance scores compared to four pioneering point-based methods. Our code is available at https://github.com/ZhangXiaotong015/GrPn.
Related papers
- Semantic Segmentation for Preoperative Planning in Transcatheter Aortic Valve Replacement [61.573750959726475]
We consider medical guidelines for preoperative planning of the transcatheter aortic valve replacement (TAVR) and identify tasks that may be supported via semantic segmentation models.<n>We first derive fine-grained TAVR-relevant pseudo-labels from coarse-grained anatomical information, in order to train segmentation models and quantify how well they are able to find these structures in the scans.
arXiv Detail & Related papers (2025-07-22T13:24:45Z) - Cardiovascular disease classification using radiomics and geometric features from cardiac CT [14.254217534681997]
We break down the CVD classification pipeline into three components: (i) image segmentation, (ii) image registration, and (iii) downstream CVD classification.<n>Specifically, we utilize the Atlas-ISTN framework and recent segmentation foundational models to generate anatomical structure segmentation and a normative healthy atlas.<n>Our experiments on the publicly available ASOCA dataset show that utilizing these features leads to better CVD classification accuracy (87.50%) when compared against classification model trained directly on raw CT images (67.50%)
arXiv Detail & Related papers (2025-06-27T13:43:05Z) - PathSegDiff: Pathology Segmentation using Diffusion model representations [63.20694440934692]
We propose PathSegDiff, a novel approach for histopathology image segmentation that leverages Latent Diffusion Models (LDMs) as pre-trained featured extractors.<n>Our method utilizes a pathology-specific LDM, guided by a self-supervised encoder, to extract rich semantic information from H&E stained histopathology images.<n>Our experiments demonstrate significant improvements over traditional methods on the BCSS and GlaS datasets.
arXiv Detail & Related papers (2025-04-09T14:58:21Z) - CTARR: A fast and robust method for identifying anatomical regions on CT images via atlas registration [0.09130220606101362]
We introduce CTARR, a novel generic method for CT Anatomical Region Recognition.
The method serves as a pre-processing step for any deep learning-based CT image analysis pipeline.
Our proposed method is based on atlas registration and provides a fast and robust way to crop any anatomical region encoded as one or multiple bounding box(es) from any unlabeled CT scan.
arXiv Detail & Related papers (2024-10-03T08:52:21Z) - Cascaded multitask U-Net using topological loss for vessel segmentation
and centerline extraction [2.264332709661011]
We propose to replace the soft-skeleton algorithm by a U-Net which computes the vascular skeleton directly from the segmentation.
We build upon this network a cascaded U-Net trained with the clDice loss to embed topological constraints during the segmentation.
arXiv Detail & Related papers (2023-07-21T14:12:28Z) - Abdominal organ segmentation via deep diffeomorphic mesh deformations [5.4173776411667935]
Abdominal organ segmentation from CT and MRI is an essential prerequisite for surgical planning and computer-aided navigation systems.
We employ template-based mesh reconstruction methods for joint liver, kidney, pancreas, and spleen segmentation.
The resulting method, UNetFlow, generalizes well to all four organs and can be easily fine-tuned on new data.
arXiv Detail & Related papers (2023-06-27T14:41:18Z) - Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via
Volumetric Pseudo-Labeling [66.75096111651062]
We created a large-scale dataset of 10,021 thoracic CTs with 157 labels.
We applied an ensemble of 3D anatomy segmentation models to extract anatomical pseudo-labels.
Our resulting segmentation models demonstrated remarkable performance on CXR.
arXiv Detail & Related papers (2023-06-06T18:01:08Z) - Using the Order of Tomographic Slices as a Prior for Neural Networks
Pre-Training [1.1470070927586016]
We propose a pre-training method SortingLoss on slices instead of volumes.
It performs pre-training on slices instead of volumes, so that a model could be fine-tuned on a sparse set of slices.
We show that the proposed method performs on par with SimCLR, while working 2x faster and requiring 1.5x less memory.
arXiv Detail & Related papers (2022-03-17T14:58:15Z) - Learning Hybrid Representations for Automatic 3D Vessel Centerline
Extraction [57.74609918453932]
Automatic blood vessel extraction from 3D medical images is crucial for vascular disease diagnoses.
Existing methods may suffer from discontinuities of extracted vessels when segmenting such thin tubular structures from 3D images.
We argue that preserving the continuity of extracted vessels requires to take into account the global geometry.
We propose a hybrid representation learning approach to address this challenge.
arXiv Detail & Related papers (2020-12-14T05:22:49Z) - 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)
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.