Segmentation Quality and Volumetric Accuracy in Medical Imaging

• URL: http://arxiv.org/abs/2404.17742v2
• Date: Tue, 14 May 2024 00:49:53 GMT
• Title: Segmentation Quality and Volumetric Accuracy in Medical Imaging
• Authors: Zheyuan Zhang, Ulas Bagci,
• Abstract summary: Current medical image segmentation relies on the region-based (Dice, F1-score) and boundary-based (Hausdorff distance, surface distance) metrics as the de-facto standard. While these metrics are widely used, they lack a unified interpretation, particularly regarding volume agreement. We utilize relative volume prediction error (vpe) to directly assess the accuracy of volume predictions derived from segmentation tasks.
• Score: 0.9426448361599084
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current medical image segmentation relies on the region-based (Dice, F1-score) and boundary-based (Hausdorff distance, surface distance) metrics as the de-facto standard. While these metrics are widely used, they lack a unified interpretation, particularly regarding volume agreement. Clinicians often lack clear benchmarks to gauge the "goodness" of segmentation results based on these metrics. Recognizing the clinical relevance of volumetry, we utilize relative volume prediction error (vpe) to directly assess the accuracy of volume predictions derived from segmentation tasks. Our work integrates theoretical analysis and empirical validation across diverse datasets. We delve into the often-ambiguous relationship between segmentation quality (measured by Dice) and volumetric accuracy in clinical practice. Our findings highlight the critical role of incorporating volumetric prediction accuracy into segmentation evaluation. This approach empowers clinicians with a more nuanced understanding of segmentation performance, ultimately improving the interpretation and utility of these metrics in real-world healthcare settings.

Related papers

• Every Component Counts: Rethinking the Measure of Success for Medical Semantic Segmentation in Multi-Instance Segmentation Tasks [60.80828925396154]
  We present Connected-Component(CC)-Metrics, a novel semantic segmentation evaluation protocol. We motivate this setup in the common medical scenario of semantic segmentation in a full-body PET/CT. We show how existing semantic segmentation metrics suffer from a bias towards larger connected components.
  arXiv  Detail & Related papers  (2024-10-24T12:26:05Z)
• Strategies to Improve Real-World Applicability of Laparoscopic Anatomy Segmentation Models [6.8726432208129555]
  We systematically analyze the impact of class characteristics, training and test data composition, and modeling parameters on eight segmentation metrics. Our findings support two adjustments to account for data biases in surgical data science.
  arXiv  Detail & Related papers  (2024-03-25T21:08:26Z)
• Semantic Textual Similarity Assessment in Chest X-ray Reports Using a Domain-Specific Cosine-Based Metric [1.7802147489386628]
  We introduce a novel approach designed specifically for assessing the semantic similarity between generated medical reports and the ground truth. Our approach is validated, demonstrating its efficiency in assessing domain-specific semantic similarity within medical contexts.
  arXiv  Detail & Related papers  (2024-02-19T07:48:25Z)
• PULASki: Learning inter-rater variability using statistical distances to improve probabilistic segmentation [36.136619420474766]
  We propose the PULASki for biomedical image segmentation that accurately captures variability in expert annotations. Our approach makes use of an improved loss function based on statistical distances in a conditional variational autoencoder structure. Our method can also be applied to a wide range of multi-label segmentation tasks and is useful for downstream tasks such as hemodynamic modelling.
  arXiv  Detail & Related papers  (2023-12-25T10:31:22Z)
• Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective [51.70661197256033]
  We propose ARCO, a semi-supervised contrastive learning framework with stratified group theory for medical image segmentation. We first propose building ARCO through the concept of variance-reduced estimation and show that certain variance-reduction techniques are particularly beneficial in pixel/voxel-level segmentation tasks. We experimentally validate our approaches on eight benchmarks, i.e., five 2D/3D medical and three semantic segmentation datasets, with different label settings.
  arXiv  Detail & Related papers  (2023-02-03T13:50:25Z)
• Towards Reliable Medical Image Segmentation by utilizing Evidential Calibrated Uncertainty [52.03490691733464]
  We introduce DEviS, an easily implementable foundational model that seamlessly integrates into various medical image segmentation networks. By leveraging subjective logic theory, we explicitly model probability and uncertainty for the problem of medical image segmentation. DeviS incorporates an uncertainty-aware filtering module, which utilizes the metric of uncertainty-calibrated error to filter reliable data.
  arXiv  Detail & Related papers  (2023-01-01T05:02:46Z)
• Theoretical analysis and experimental validation of volume bias of soft Dice optimized segmentation maps in the context of inherent uncertainty [6.692460499366963]
  Recent segmentation methods use a differentiable surrogate metric, such as soft Dice, as part of the loss function during the learning phase. We first briefly describe how to derive volume estimates from a segmentation that is, potentially, inherently uncertain or ambiguous. We find that, even though soft Dice optimization leads to an improved performance with respect to the Dice score and other measures, it may introduce a volume bias for tasks with high inherent uncertainty.
  arXiv  Detail & Related papers  (2022-11-08T11:04:52Z)
• MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learning [92.91544082745196]
  Federated learning (FL) has been widely employed for medical image analysis. FL's performance is limited for multiple sclerosis (MS) lesion segmentation tasks. We propose the first FL MS lesion segmentation framework via two effective re-weighting mechanisms.
  arXiv  Detail & Related papers  (2022-05-03T14:06:03Z)
• Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation [92.9634065964963]
  We present a new semi-supervised segmentation model, namely, conservative-radical network (CoraNet) based on our uncertainty estimation and separate self-training strategy. Compared with the current state of the art, our CoraNet has demonstrated superior performance.
  arXiv  Detail & Related papers  (2021-10-17T08:49:33Z)
• Systematic Clinical Evaluation of A Deep Learning Method for Medical Image Segmentation: Radiosurgery Application [48.89674088331313]
  We systematically evaluate a Deep Learning (DL) method in a 3D medical image segmentation task. Our method is integrated into the radiosurgery treatment process and directly impacts the clinical workflow.
  arXiv  Detail & Related papers  (2021-08-21T16:15:40Z)

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.