Automated Thoracolumbar Stump Rib Detection and Analysis in a Large CT Cohort

• URL: http://arxiv.org/abs/2505.05004v1
• Date: Thu, 08 May 2025 07:21:12 GMT
• Title: Automated Thoracolumbar Stump Rib Detection and Analysis in a Large CT Cohort
• Authors: Hendrik Möller, Hanna Schön, Alina Dima, Benjamin Keinert-Weth, Robert Graf, Matan Atad, Johannes Paetzold, Friederike Jungmann, Rickmer Braren, Florian Kofler, Bjoern Menze, Daniel Rueckert, Jan S. Kirschke,
• Abstract summary: This study aims to automate thoracolumbar stump rib detection and analyze their morphology quantitatively.<n>We train a high-resolution deep-learning model for rib segmentation and show significant improvements compared to existing models.<n>We show that with partially visible ribs, these features can achieve an F1-score of 0.84 in differentiating stump ribs from regular ones.
• Score: 8.555532200080874
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Thoracolumbar stump ribs are one of the essential indicators of thoracolumbar transitional vertebrae or enumeration anomalies. While some studies manually assess these anomalies and describe the ribs qualitatively, this study aims to automate thoracolumbar stump rib detection and analyze their morphology quantitatively. To this end, we train a high-resolution deep-learning model for rib segmentation and show significant improvements compared to existing models (Dice score 0.997 vs. 0.779, p-value < 0.01). In addition, we use an iterative algorithm and piece-wise linear interpolation to assess the length of the ribs, showing a success rate of 98.2%. When analyzing morphological features, we show that stump ribs articulate more posteriorly at the vertebrae (-19.2 +- 3.8 vs -13.8 +- 2.5, p-value < 0.01), are thinner (260.6 +- 103.4 vs. 563.6 +- 127.1, p-value < 0.01), and are oriented more downwards and sideways within the first centimeters in contrast to full-length ribs. We show that with partially visible ribs, these features can achieve an F1-score of 0.84 in differentiating stump ribs from regular ones. We publish the model weights and masks for public use.

Related papers

• Longitudinal Assessment of Lung Lesion Burden in CT [4.010883532297142]
  In the U.S., lung cancer is the second major cause of death.<n>Many approaches for lung nodule segmentation and volumetric analysis have been proposed, but few have looked at longitudinal changes in total lung tumor burden.<n>In this work, we trained two 3D models (nnUNet) with and without anatomical priors to automatically segment lung lesions and quantified total lesion burden for each patient.
  arXiv  Detail & Related papers  (2025-04-09T14:30:43Z)
• SpineFM: Leveraging Foundation Models for Automatic Spine X-ray Segmentation [0.0]
  This paper introduces SpineFM, a novel pipeline that achieves state-of-the-art performance in the automatic segmentation and identification of vertebral bodies.<n>We achieved outstanding results on two publicly available spine X-Ray datasets, with successful identification of 97.8% and 99.6% of annotated vertebrae.
  arXiv  Detail & Related papers  (2024-11-01T02:51:21Z)
• Attention-based Saliency Maps Improve Interpretability of Pneumothorax Classification [52.77024349608834]
  To investigate chest radiograph (CXR) classification performance of vision transformers (ViT) and interpretability of attention-based saliency. ViTs were fine-tuned for lung disease classification using four public data sets: CheXpert, Chest X-Ray 14, MIMIC CXR, and VinBigData. ViTs had comparable CXR classification AUCs compared with state-of-the-art CNNs.
  arXiv  Detail & Related papers  (2023-03-03T12:05:41Z)
• Automatic Classification of Symmetry of Hemithoraces in Canine and Feline Radiographs [0.0]
  We propose a hemithoraces segmentation method based on Convolutional Neural Networks (CNNs) and active contours. To test the robustness of the proposed thorax segmentation method to underexposure and overexposure, we synthetically corrupted properly exposed radiographs.
  arXiv  Detail & Related papers  (2023-02-24T22:46:16Z)
• RibSeg v2: A Large-scale Benchmark for Rib Labeling and Anatomical Centerline Extraction [49.715490897822264]
  We extend our prior dataset (RibSeg) on the binary rib segmentation task to a comprehensive benchmark, named RibSeg v2. Based on RibSeg v2, we develop a pipeline including deep learning-based methods for rib labeling, and a skeletonization-based method for centerline extraction.
  arXiv  Detail & Related papers  (2022-10-18T00:55:37Z)
• TotalSegmentator: robust segmentation of 104 anatomical structures in CT images [48.50994220135258]
  We present a deep learning segmentation model for body CT images. The model can segment 104 anatomical structures relevant for use cases such as organ volumetry, disease characterization, and surgical or radiotherapy planning.
  arXiv  Detail & Related papers  (2022-08-11T15:16:40Z)
• RibSeg Dataset and Strong Point Cloud Baselines for Rib Segmentation from CT Scans [62.16198969529679]
  Manual rib inspections in computed tomography (CT) scans are clinically critical but labor-intensive. We develop a labeled rib segmentation benchmark, named emphRibSeg, including 490 CT scans (11,719 individual ribs) from a public dataset. We thresholded and sampled sparse voxels from the input and designed a point cloud-based baseline method for rib segmentation.
  arXiv  Detail & Related papers  (2021-09-17T16:17:35Z)
• Deep Learning to Quantify Pulmonary Edema in Chest Radiographs [7.121765928263759]
  We developed a machine learning model to classify the severity grades of pulmonary edema on chest radiographs. Deep learning models were trained on a large chest radiograph dataset.
  arXiv  Detail & Related papers  (2020-08-13T15:45:44Z)
• Automated Identification of Thoracic Pathology from Chest Radiographs with Enhanced Training Pipeline [0.0]
  We use the currently largest publicly available dataset ChestX-ray14 of 112, chest radiographs of 30,805 patients. Each image was annotated with either a 'NoFinding' class, or one or more of 14 thoracic pathology labels. We encoded labels as binary vectors using k-hot encoding.
  arXiv  Detail & Related papers  (2020-06-11T20:43:09Z)
• Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT [48.785596536318884]
  The proposed method takes as input a non-contrasted chest CT and segments the lesions, lungs, and lobes in three dimensions. The method outputs two combined measures of the severity of lung and lobe involvement, quantifying both the extent of COVID-19 abnormalities and presence of high opacities. Evaluation of the algorithm is reported on CTs of 200 participants (100 COVID-19 confirmed patients and 100 healthy controls) from institutions from Canada, Europe and the United States.
  arXiv  Detail & Related papers  (2020-04-02T21:49:14Z)
• Machine-Learning-Based Multiple Abnormality Prediction with Large-Scale Chest Computed Tomography Volumes [64.21642241351857]
  We curated and analyzed a chest computed tomography (CT) data set of 36,316 volumes from 19,993 unique patients. We developed a rule-based method for automatically extracting abnormality labels from free-text radiology reports. We also developed a model for multi-organ, multi-disease classification of chest CT volumes.
  arXiv  Detail & Related papers  (2020-02-12T00:59:23Z)

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.