Influence of Geometry, Class Imbalance and Alignment on Reconstruction Accuracy -- A Micro-CT Phantom-Based Evaluation

• URL: http://arxiv.org/abs/2602.07658v1
• Date: Sat, 07 Feb 2026 18:33:34 GMT
• Title: Influence of Geometry, Class Imbalance and Alignment on Reconstruction Accuracy -- A Micro-CT Phantom-Based Evaluation
• Authors: Avinash Kumar K M, Samarth S. Raut,
• Abstract summary: The accuracy of the 3D models created from medical scans depends on imaging hardware, segmentation methods and mesh processing techniques.<n>This work evaluates the errors across the reconstruction pipeline and explores the use of voxel and surface-based accuracy metrics.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The accuracy of the 3D models created from medical scans depends on imaging hardware, segmentation methods and mesh processing techniques etc. The effects of geometry type, class imbalance, voxel and point cloud alignment on accuracy remain to be thoroughly explored. This work evaluates the errors across the reconstruction pipeline and explores the use of voxel and surface-based accuracy metrics for different segmentation algorithms and geometry types. A sphere, a facemask, and an AAA were printed using the SLA technique and scanned using a micro-CT machine. Segmentation was performed using GMM, Otsu and RG based methods. Segmented and reference models aligned using the KU algorithm, were quantitatively compared to evaluate metrics like Dice and Jaccard scores, precision. Surface meshes were registered with reference meshes using an ICP-based alignment process. Metrics like chamfer distance, and average Hausdorff distance were evaluated. The Otsu method was found to be the most suitable method for all the geometries. AAA yielded low overlap scores due to its small wall thickness and misalignment. The effect of class imbalance on specificity was observed the most for AAA. Surface-based accuracy metrics differed from the voxel-based trends. The RG method performed best for sphere, while GMM and Otsu perform better for AAA. The facemask surface was most error-prone, possibly due to misalignment during the ICP process. Segmentation accuracy is a cumulative sum of errors across different stages of the reconstruction process. High voxel-based accuracy metrics may be misleading in cases of high class imbalance and sensitivity to alignment. The Jaccard index is found to be more stringent than the Dice and more suitable for accuracy assessment for thin-walled structures. Voxel and point cloud alignment should be ensured to make any reliable assessment of the reconstruction pipeline.

Related papers

• MS-ISSM: Objective Quality Assessment of Point Clouds Using Multi-scale Implicit Structural Similarity [65.85858856481131]
  unstructured and irregular nature of point clouds poses a significant challenge for objective quality assessment (PCQA)<n>We propose the Multi-scale Implicit Structural Similarity Measurement (MS-ISSM)
  arXiv  Detail & Related papers  (2026-01-03T14:58:52Z)
• 3D Face Reconstruction Error Decomposed: A Modular Benchmark for Fair and Fast Method Evaluation [30.625439879741847]
  We present a toolkit for a Modularized 3D Face reconstruction Benchmark (M3DFB)<n>The fundamental components of error are segregated and interchangeable, allowing one to quantify the effect of each.<n>We propose a new component, namely correction, and present a computationally efficient approach that penalizes for mesh topology inconsistency.
  arXiv  Detail & Related papers  (2025-05-23T15:28:43Z)
• Approximate Size Targets Are Sufficient for Accurate Semantic Segmentation [52.239136918460616]
  Extending binary class tags to approximate relative object-size distributions allows off-the-shelf architectures to solve the segmentation problem.<n>A straightforward zero-avoiding KL-divergence loss for average predictions produces segmentation accuracy comparable to the standard pixel-precise supervision.<n>Our ideas are validated on PASCAL VOC using our new human annotations of approximate object sizes.
  arXiv  Detail & Related papers  (2025-03-10T06:02:13Z)
• Quality assurance of organs-at-risk delineation in radiotherapy [7.698565355235687]
  The delineation of tumor target and organs-at-risk is critical in the radiotherapy treatment planning. The quality assurance of the automatic segmentation is still an unmet need in clinical practice. Our proposed model, which introduces residual network and attention mechanism in the one-class classification framework, was able to detect the various types of OAR contour errors with high accuracy.
  arXiv  Detail & Related papers  (2024-05-20T02:32:46Z)
• Revisiting Evaluation Metrics for Semantic Segmentation: Optimization and Evaluation of Fine-grained Intersection over Union [113.20223082664681]
  We propose the use of fine-grained mIoUs along with corresponding worst-case metrics. These fine-grained metrics offer less bias towards large objects, richer statistical information, and valuable insights into model and dataset auditing. Our benchmark study highlights the necessity of not basing evaluations on a single metric and confirms that fine-grained mIoUs reduce the bias towards large objects.
  arXiv  Detail & Related papers  (2023-10-30T03:45:15Z)
• Unleash the Potential of 3D Point Cloud Modeling with A Calibrated Local Geometry-driven Distance Metric [62.365983810610985]
  We propose a novel distance metric called Calibrated Local Geometry Distance (CLGD) CLGD computes the difference between the underlying 3D surfaces calibrated and induced by a set of reference points. As a generic metric, CLGD has the potential to advance 3D point cloud modeling.
  arXiv  Detail & Related papers  (2023-06-01T11:16:20Z)
• A Generalized Surface Loss for Reducing the Hausdorff Distance in Medical Imaging Segmentation [1.2289361708127877]
  We propose a novel loss function to minimize Hausdorff-based metrics with more desirable numerical properties than current methods. Our loss function outperforms other losses when tested on the LiTS and BraTS datasets using the state-of-the-art nnUNet architecture.
  arXiv  Detail & Related papers  (2023-02-08T04:01:42Z)
• Lerna: Transformer Architectures for Configuring Error Correction Tools for Short- and Long-Read Genome Sequencing [5.911600622951255]
  We introduce Lerna for the automated configuration of k-mer-based EC tools. We show that the best k-mer value can vary for different datasets, even for the same EC tool. We also show that our attention-based models have significant runtime improvement for the entire pipeline.
  arXiv  Detail & Related papers  (2021-12-19T05:59:26Z)
• LSG-CPD: Coherent Point Drift with Local Surface Geometry for Point Cloud Registration [1.8876415010297891]
  We propose a novel method called CPD with Local Surface Geometry (LSG-CPD) for rigid point cloud registration. Our method adaptively adds different levels of point-to-plane penalization on top of the point-to-point penalization based on the flatness of the local surface. It is significantly faster than modern implementations of CPD.
  arXiv  Detail & Related papers  (2021-03-28T03:46:41Z)
• Surface Warping Incorporating Machine Learning Assisted Domain Likelihood Estimation: A New Paradigm in Mine Geology Modelling and Automation [68.8204255655161]
  A Bayesian warping technique has been proposed to reshape modeled surfaces based on geochemical and spatial constraints imposed by newly acquired blasthole data. This paper focuses on incorporating machine learning in this warping framework to make the likelihood generalizable. Its foundation is laid by a Bayesian computation in which the geological domain likelihood given the chemistry, p(g|c) plays a similar role to p(y(c)|g.
  arXiv  Detail & Related papers  (2021-02-15T10:37:52Z)
• Making Affine Correspondences Work in Camera Geometry Computation [62.7633180470428]
  Local features provide region-to-region rather than point-to-point correspondences. We propose guidelines for effective use of region-to-region matches in the course of a full model estimation pipeline. Experiments show that affine solvers can achieve accuracy comparable to point-based solvers at faster run-times.
  arXiv  Detail & Related papers  (2020-07-20T12:07:48Z)

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.