Can Unsupervised Methods Outperform Supervised Deep Learning When Ground Truth Is Sparse? A Case Study of Bronchovascular Bundle Segmentation in Low-Dose CT

• URL: http://arxiv.org/abs/2608.16855v1
• Date: Mon, 17 Aug 2026 17:38:22 GMT
• Title: Can Unsupervised Methods Outperform Supervised Deep Learning When Ground Truth Is Sparse? A Case Study of Bronchovascular Bundle Segmentation in Low-Dose CT
• Abstract summary: Lung cancer remains the deadliest cancer worldwide because it is often diagnosed too late.<n>In early stage lung cancer, nodule visibility is reduced by adjacent blood vessels and airway walls.<n> Task-specific analysis of the bronchovascular bundle is important for efficient nodule detection.
• Score: 30.05221274603036
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Background Lung cancer remains the deadliest cancer worldwide because it is often diagnosed too late. Effective treatment depends on detection at an early screening stage. However, the growing number of patients and the limited number of radiologists lead to prolonged diagnostic waiting times. In very early stage lung cancer, nodule visibility is further reduced by adjacent blood vessels and airway walls, because nodules are often connected to or supplied by these structures. Task-specific analysis of the bronchovascular bundle is therefore important for efficient nodule detection, and its removal can increase the diagnostic potential of lung cancer screening. Materials and Methods To assess the efficacy of the proposed method, we used series from widely utilized LDCT datasets, including the Duke Lung Cancer Screening (DLCS) dataset and the Pilot Pomeranian Lung Cancer Screening Program. The proposed bronchovascular bundle segmentation pipeline, RONALD, operates on computed tomography images and returns binary masks of vessels and bronchi located in the lung parenchyma. The method includes a preprocessing stage with lung, lobe, and mediastinum segmentation, followed by separate vessel and bronchial tree segmentation. Results The proposed pipeline segmented the bronchovascular bundle in low-dose computed tomography scans while improving nodule retention compared with other segmentation methods: from 93.98% and 90.36% to 100% in DLCS, and from 83.16% and 62.36% to 99.92% in the Pomeranian dataset. Conclusion The resulting segmentations can improve lung nodule detection in the very early stages of lung cancer.

Related papers

• LungCRCT: Causal Representation based Lung CT Processing for Lung Cancer Treatment [3.765413696274397]
  LungCRCT is a latent causal representation learning based lung cancer analysis framework.<n>It retrieves causal representations of factors within the physical causal mechanism of lung cancer progression.
  arXiv  Detail & Related papers  (2026-01-26T04:03:50Z)
• X-ray Insights Unleashed: Pioneering the Enhancement of Multi-Label Long-Tail Data [86.52299247918637]
  Long-tailed pulmonary anomalies in chest radiography present formidable diagnostic challenges.<n>Despite the recent strides in diffusion-based methods for enhancing the representation of tailed lesions, the paucity of rare lesion exemplars curtails the generative capabilities of these approaches.<n>We propose a novel data synthesis pipeline designed to augment tail lesions utilizing a copious supply of conventional normal X-rays.
  arXiv  Detail & Related papers  (2025-12-24T06:14:55Z)
• LungEvaty: A Scalable, Open-Source Transformer-based Deep Learning Model for Lung Cancer Risk Prediction in LDCT Screening [37.29507297342265]
  LungEvaty is a transformer-based framework for predicting 1-6 year lung cancer risk from a single LDCT scan.<n>It learns directly from large-scale screening data to capture comprehensive anatomical and pathological cues relevant for malignancy risk.<n>LungEvaty was trained on more than 90,000 CT scans, including over 28,000 for fine-tuning and 6,000 for evaluation.
  arXiv  Detail & Related papers  (2025-11-25T09:38:10Z)
• Ultrasound Lung Aeration Map via Physics-Aware Neural Operators [78.6077820217471]
  Lung ultrasound is a growing modality in clinics for diagnosing acute and chronic lung diseases.<n>It is complicated by complex reverberations from the pleural interface caused by the inability of ultrasound to penetrate air.<n>We propose LUNA, an AI model that directly reconstructs lung aeration maps from RF data.
  arXiv  Detail & Related papers  (2025-01-02T09:24:34Z)
• Lung-CADex: Fully automatic Zero-Shot Detection and Classification of Lung Nodules in Thoracic CT Images [45.29301790646322]
  Computer-aided diagnosis can help with early lung nodul detection and facilitate subsequent nodule characterization. We propose CADe, for segmenting lung nodules in a zero-shot manner using a variant of the Segment Anything Model called MedSAM. We also propose, CADx, a method for the nodule characterization as benign/malignant by making a gallery of radiomic features and aligning image-feature pairs through contrastive learning.
  arXiv  Detail & Related papers  (2024-07-02T19:30:25Z)
• SGDA: Towards 3D Universal Pulmonary Nodule Detection via Slice Grouped Domain Attention [47.44114201293201]
  Lung cancer is the leading cause of cancer death worldwide. Current pulmonary nodule detection methods are usually domain-specific. We propose a slice grouped domain attention (SGDA) module to enhance the generalization capability of the pulmonary nodule detection networks.
  arXiv  Detail & Related papers  (2023-03-07T03:17:49Z)
• Lung nodules segmentation from CT with DeepHealth toolkit [6.980270783615686]
  The goal of this study was to demonstrate the feasibility of Deephealth toolkit including PyECVL and PyEDDL libraries to precisely segment lung nodules. The results depict accurate segmentation of lung nodules across a wide diameter range and better accuracy over a traditional detection approach.
  arXiv  Detail & Related papers  (2022-08-01T06:54:12Z)
• BronchusNet: Region and Structure Prior Embedded Representation Learning for Bronchus Segmentation and Classification [53.53758990624962]
  We propose a region and structure prior embedded framework named BronchusNet to achieve accurate bronchial analysis. For bronchus segmentation, we propose an adaptive hard region-aware UNet that incorporates multi-level prior guidance of hard pixel-wise samples. For the classification of bronchial branches, we propose a hybrid point-voxel graph learning module.
  arXiv  Detail & Related papers  (2022-05-14T02:32:33Z)
• Enhancing Cancer Prediction in Challenging Screen-Detected Incident Lung Nodules Using Time-Series Deep Learning [2.744770849264355]
  Lung cancer screening (LCS) using annual low-dose computed tomography (CT) scanning has been proven to significantly reduce lung cancer mortality. Improving risk stratification of malignancy risk in lung nodules can be enhanced using machine/deep learning algorithms. Here we show the performance of our time-series deep learning model (DeepCAD-NLM-L) which integrates multi-model information across three longitudinal data domains.
  arXiv  Detail & Related papers  (2022-03-30T18:40:36Z)
• CoRSAI: A System for Robust Interpretation of CT Scans of COVID-19 Patients Using Deep Learning [133.87426554801252]
  We adopted an approach based on using an ensemble of deep convolutionalneural networks for segmentation of lung CT scans. Using our models we are able to segment the lesions, evaluatepatients dynamics, estimate relative volume of lungs affected by lesions and evaluate the lung damage stage.
  arXiv  Detail & Related papers  (2021-05-25T12:06:55Z)
• Development of a Multi-Task Learning V-Net for Pulmonary Lobar Segmentation on Computed Tomography and Application to Diseased Lungs [0.19573380763700707]
  Diseased lung regions often produce high-density zones on CT images, limiting an algorithm's execution to specify damaged lobes. This impact motivated developing an improved machine learning method to segment lung lobes. The approach can be readily adopted in the clinical setting as a robust tool for radiologists.
  arXiv  Detail & Related papers  (2021-05-11T17:10:25Z)
• M3Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening from CT Imaging [85.00066186644466]
  We propose a Multi-task Multi-slice Deep Learning System (M3Lung-Sys) for multi-class lung pneumonia screening from CT imaging. In addition to distinguish COVID-19 from Healthy, H1N1, and CAP cases, our M 3 Lung-Sys also be able to locate the areas of relevant lesions.
  arXiv  Detail & Related papers  (2020-10-07T06:22:24Z)

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.