Automated cross-sectional view selection in CT angiography of aortic dissections with uncertainty awareness and retrospective clinical annotations

• URL: http://arxiv.org/abs/2111.11269v1
• Date: Mon, 22 Nov 2021 15:11:36 GMT
• Title: Automated cross-sectional view selection in CT angiography of aortic dissections with uncertainty awareness and retrospective clinical annotations
• Abstract summary: We show how manual annotations routinely collected in a clinic can be efficiently used to ease this task. Ill-posed but repetitive imaging tasks can be eased or automated by leveraging imperfect, retrospective clinical annotations.
• Score: 11.415942647070796
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Objective: Surveillance imaging of chronic aortic diseases, such as dissections, relies on obtaining and comparing cross-sectional diameter measurements at predefined aortic landmarks, over time. Due to a lack of robust tools, the orientation of the cross-sectional planes is defined manually by highly trained operators. We show how manual annotations routinely collected in a clinic can be efficiently used to ease this task, despite the presence of a non-negligible interoperator variability in the measurements. Impact: Ill-posed but repetitive imaging tasks can be eased or automated by leveraging imperfect, retrospective clinical annotations. Methodology: In this work, we combine convolutional neural networks and uncertainty quantification methods to predict the orientation of such cross-sectional planes. We use clinical data randomly processed by 11 operators for training, and test on a smaller set processed by 3 independent operators to assess interoperator variability. Results: Our analysis shows that manual selection of cross-sectional planes is characterized by 95% limits of agreement (LOA) of $10.6^\circ$ and $21.4^\circ$ per angle. Our method showed to decrease static error by $3.57^\circ$ ($40.2$%) and $4.11^\circ$ ($32.8$%) against state of the art and LOA by $5.4^\circ$ ($49.0$%) and $16.0^\circ$ ($74.6$%) against manual processing. Conclusion: This suggests that pre-existing annotations can be an inexpensive resource in clinics to ease ill-posed and repetitive tasks like cross-section extraction for surveillance of aortic dissections.

Related papers

• Anatomy-Change-Aware Bidirectional Selective State-Space Memory for Clinically Deployed Thoracic Radiotherapy Auto-Contouring [0.6439594960617159]
  DAMM-Net++ is a 2.5D architecture for thoracic radiotherapy auto-contouring.<n>It addresses inter-slice surface incoherence, systematic failure on small low-contrast targets, and the absence of per-case reliability signals.<n>It achieves a mean Dice of 0.955 and HD95 of 3.78 mm, with the largest gains on low-contrast organs-at-risk.
  arXiv  Detail & Related papers  (2026-09-11T11:13:06Z)
• Synergistic Perception-Reasoning Governance: Grounding Medical MLLMs with Verifiable Anatomical Evidence [45.78409219344429]
  Multimodal large language models (MLLMs) show strong promise for clinical VQA and radiology report generation, yet inference-time hallucinations undermine trustworthy use.<n>We propose a holistic, training-free evidence-injection framework that systematically mitigates hallucinations through dual-side evidence injection.<n>We conduct systematic evaluations on 2 tasks and 5 datasets using textttLLaVA-1.5-7B, texttLLaVA-Med-1.5-7B, texttQwen3-VL-8B/32B, and textttInternVL
  arXiv  Detail & Related papers  (2026-06-30T08:07:25Z)
• Beyond Scalar Scores: Exploring LLM-based Metrics for Clinical Significance Evaluation in Radiology Reports [49.5225801722164]
  Existing metrics obscure this requirement by reducing report quality to a medically ungrounded scalar.<n>We study this boundary using ReEvalMed benchmark as testbed and evaluate metric-level clinical significance.
  arXiv  Detail & Related papers  (2026-06-17T08:10:30Z)
• A New Angle on Bones: Robust Pose Estimation in X-Ray and Ultrasound [5.592342871855084]
  Measuring the angle between bone structures is a routine task in medical image analysis and provides a key quantitative parameter for diagnosis and treatment planning.<n>We address automatic bone pose estimation using a learning-based point candidate proposal followed by a line model to extract axis parameters.<n>We evaluate our method on three clinically relevant paediatric angle estimation tasks: fracture fragment assessment in radiographs and ultrasound and developmental dysplasia of the hip evaluation in ultrasound.
  arXiv  Detail & Related papers  (2026-06-03T10:25:08Z)
• Training-free Detection and 6D Pose Estimation of Unseen Surgical Instruments [45.387920812487465]
  This work introduces a training-free pipeline for accurate multi-view 6D pose estimation of unseen surgical instruments.<n>The proposed method was rigorously evaluated on real-world surgical data from the MVPSP dataset.
  arXiv  Detail & Related papers  (2026-03-26T09:28:19Z)
• Dealing with Segmentation Errors in Needle Reconstruction for MRI-Guided Brachytherapy [0.0]
  We propose adaptations to existing post-processing techniques aimed at dealing with segmentation errors and improving the reconstruction accuracy.<n> Experiments on a prostate cancer dataset, based on MRI scans annotated by medical professionals, demonstrate that our proposed adaptations can help to effectively manage segmentation errors.
  arXiv  Detail & Related papers  (2025-07-25T02:35:04Z)
• 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)
• ProstaTD: Bridging Surgical Triplet from Classification to Fully Supervised Detection [54.270188252068145]
  ProstaTD is a large-scale dataset for surgical triplet detection developed from the technically demanding domain of robot-assisted prostatectomy.<n>The dataset comprises 71,775 video frames and 196,490 annotated triplet instances, collected from 21 surgeries performed across multiple institutions.<n>ProstaTD is the largest and most diverse surgical triplet dataset to date, moving the field from simple classification to full detection with precise spatial and temporal boundaries.
  arXiv  Detail & Related papers  (2025-06-01T19:29:39Z)
• Towards Patient-Specific Surgical Planning for Bicuspid Aortic Valve Repair: Fully Automated Segmentation of the Aortic Valve in 4D CT [0.0732099897993399]
  The bicuspid aortic valve (BAV) is the most prevalent congenital heart defect and may require surgery for complications such as stenosis, regurgitation, and aortopathy. Contrast-enhanced 4D computed tomography (CT) produces volumetric temporal sequences with excellent contrast and spatial resolution. Deep learning-based methods are capable of fully automated segmentation, but no BAV-specific model exists.
  arXiv  Detail & Related papers  (2025-02-13T22:43:43Z)
• 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)
• Automated Identification of Failure Cases in Organ at Risk Segmentation Using Distance Metrics: A Study on CT Data [0.19661503834671132]
  Automated organ at risk (OAR) segmentation is crucial for radiation therapy planning in CT scans. The paper proposes a method to automatically identify failure cases by setting a threshold for the combination of Dice and Hausdorff distances.
  arXiv  Detail & Related papers  (2023-08-21T11:14:49Z)
• 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)
• Adversarial Transformer for Repairing Human Airway Segmentation [7.176060570019899]
  This paper presents a patch-scale adversarial-based refinement network that takes in preliminary segmentation along with original CT images and outputs a refined mask of the airway structure. The results are quantitatively evaluated by seven metrics and achieved more than a 15% rise in detected length ratio and detected branch ratio.
  arXiv  Detail & Related papers  (2022-10-21T15:20:08Z)
• Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation [67.19443246236048]
  Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung diseases. Some small-sized airway branches (e.g., bronchus and terminaloles) significantly aggravate the difficulty of automatic segmentation. This paper presents an efficient method for airway segmentation, comprising a novel fuzzy attention neural network and a comprehensive loss function.
  arXiv  Detail & Related papers  (2022-09-05T16:38:13Z)
• A Deep Learning Approach to Predicting Collateral Flow in Stroke Patients Using Radiomic Features from Perfusion Images [58.17507437526425]
  Collateral circulation results from specialized anastomotic channels which provide oxygenated blood to regions with compromised blood flow. The actual grading is mostly done through manual inspection of the acquired images. We present a deep learning approach to predicting collateral flow grading in stroke patients based on radiomic features extracted from MR perfusion data.
  arXiv  Detail & Related papers  (2021-10-24T18:58:40Z)
• 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)
• Controlling False Positive/Negative Rates for Deep-Learning-Based Prostate Cancer Detection on Multiparametric MR images [58.85481248101611]
  We propose a novel PCa detection network that incorporates a lesion-level cost-sensitive loss and an additional slice-level loss based on a lesion-to-slice mapping function. Our experiments based on 290 clinical patients concludes that 1) The lesion-level FNR was effectively reduced from 0.19 to 0.10 and the lesion-level FPR was reduced from 1.03 to 0.66 by changing the lesion-level cost.
  arXiv  Detail & Related papers  (2021-06-04T09:51:27Z)
• Automated Detection of Coronary Artery Stenosis in X-ray Angiography using Deep Neural Networks [0.0]
  We propose a two-step deep-learning framework to partially automate the detection of stenosis from X-ray coronary angiography images. We achieved a 0.97 accuracy on the task of classifying the Left/Right Coronary Artery angle view and 0.68/0.73 recall on the determination of the regions of interest, for LCA and RCA, respectively.
  arXiv  Detail & Related papers  (2021-03-04T11:45:54Z)
• Assisted Probe Positioning for Ultrasound Guided Radiotherapy Using Image Sequence Classification [55.96221340756895]
  Effective transperineal ultrasound image guidance in prostate external beam radiotherapy requires consistent alignment between probe and prostate at each session during patient set-up. We demonstrate a method for ensuring accurate probe placement through joint classification of images and probe position data. Using a multi-input multi-task algorithm, spatial coordinate data from an optically tracked ultrasound probe is combined with an image clas-sifier using a recurrent neural network to generate two sets of predictions in real-time. The algorithm identified optimal probe alignment within a mean (standard deviation) range of 3.7$circ$ (1.2$circ$) from
  arXiv  Detail & Related papers  (2020-10-06T13:55:02Z)
• Segmentation-free Estimation of Aortic Diameters from MRI Using Deep Learning [2.231365407061881]
  We propose a supervised deep learning method for the direct estimation of aortic diameters. Our approach makes use of a 3D+2D convolutional neural network (CNN) that takes as input a 3D scan and outputs the aortic diameter at a given location. Overall, the 3D+2D CNN achieved a mean absolute error between 2.2-2.4 mm depending on the considered aortic location.
  arXiv  Detail & Related papers  (2020-09-09T18:28:00Z)

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.