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

Anna Mrukwa; Marek Socha; Aleksandra Suwalska; Agata Durawa; Malgorzata Jelitto; Katarzyna Dziadziuszko; Edyta Szurowska; Pawel Bozek

cs.CVAug 17, 2026

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

Authors: Anna Mrukwa, Marek Socha, Aleksandra Suwalska, Agata Durawa, Malgorzata Jelitto, Katarzyna Dziadziuszko, Edyta Szurowska, Pawel Bozek, +4 more

Organizations: Department of Data Science and Engineering, Silesian University of Technology, Gliwice, Poland · Department of Thoracic Surgery, Medical University of Gdansk, Gdansk, Poland · Department of Thoracic Surgery, Medical University of Gdańsk, Gdańsk, Poland · 2nd Division of Radiology, Medical University of Gdansk, Gdansk, Poland · Department of Radiology and Radiodiagnostics, Medical University of Silesia, Katowice, Poland · Department of Breast Medical Oncology, Yale School of Medicine, New Haven, CT, USA · Department of Oncology and Radiotherapy, Medical University of Gdansk, Gdansk, Poland · Department of Oncology and Radiotherapy, Medical University of Gdańsk, Gdańsk, Poland · Department of Data Science and Engineering, Faculty of Automatic Control, Electronics and Computer Science, Akademicka 16, 44-100 Gliwice, Poland

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.

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Giulia Romoli, Filippo Ruffini, Francesco Di Feola +3

Department of Diagnostics and Intervention, Radiation Physics, Biomedical Engineering, Umeå University, Universitetstorget 4, Umeå, Sweden. · Unit of Artificial Intelligence and Computer Systems, Department of Engineering, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, Rome, Italy. · Department of Naval, Electrical, Electronic, and Telecommunications Engineering, University of Genoa, Via Opera Pia 11A, Genoa, Italy.

Medical Image Segmentation 3D Medical Image Segmentation Medical Image Anomaly Detection Longitudinal Medical Image Analysis

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Sep 10, 2026

GRIPNet: Gaussian Radial Intensity Prior Guided Architecture for Pulmonary Nodule Detection in CT

Lung cancer causes more deaths than any other malignancy, and low-dose CT screening is the main pathway to early diagnosis. That pathway hinges on the smallest lesions, yet nodules below six millimeters remain hard to detect, because most methods treat a nodule as a generic object and ignore the imaging physics behind its appearance. We show that this appearance is highly regular. Intensity peaks at the geometric center of a nodule and decays radially in a Gaussian pattern, and a fit to 18,218 annotated lesions from three public benchmarks yields a mean radial coefficient of determination above 0.86 in every dataset and size stratum. A square convolution samples both axes uniformly and is mismatched to this radial signal, most severely for small nodules. Guided by this evidence, we propose GRIPNet (Gaussian Radial Intensity Prior Network), a detector in which every module maps to a measurable property of the intensity distribution. Pinwheel convolutions decompose radial gradients, a dual-frequency module separates boundary detail from structural context, dilated masked attention matches the decay extent, and an adaptive loss reweights samples by conspicuity. GRIPNet raises [email protected] to 95.3, 91.6 and 97.9 percent on KanserSet, LUNA16 and Lung-PET-CT-Dx while sharpening high-IoU localization at real-time speed.

Haojie Yang, Ran Su

College of Intelligence and Computing, Tianjin University, Tianjin, China

Small Object Detection Medical Image Anomaly Detection Computed Tomography Medical Image Localization

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University of Michigan · University of Michigan Medical School

Medical Image Analysis Longitudinal Medical Image Analysis Medical Image Synthesis Computed Tomography Multimodal Diffusion Models

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