NoduleFlow: Automatic 3D Lung Nodule Detection and Segmentation

Giulia Romoli; Filippo Ruffini; Francesco Di Feola; Valerio Guarrasi; Matteo Tortora; Paolo Soda

cs.CVOct 12, 2026

NoduleFlow: Automatic 3D Lung Nodule Detection and Segmentation

Authors: Giulia Romoli, Filippo Ruffini, Francesco Di Feola, Valerio Guarrasi, Matteo Tortora, Paolo Soda

Organizations: 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.

Abstract

Low dose Computed Tomography (CT) is the standard imaging modality for lung cancer screening. On CT scans, lung cancer typically manifests as pulmonary nodules, whose manual identification is time-consuming and prone to observer variability, image quality limitations, and suboptimal reading conditions. Moreover, subtle temporal changes are often difficult to quantify reliably in CT scans from the same patient across follow-up examinations. Deep learning-based approaches for the automatic detection and segmentation of lung nodules have demonstrated improved accuracy and efficiency. However, most existing approaches provide only coarse region-of-interest localisation, rely on prior lesion annotations or human supervision, or treat detection and segmentation as separate tasks. In this work, we present NoduleFlow, a framework for lung nodule detection, classification, and segmentation directly from series of CT volumes. Our proposal consists of four interconnected modules and operates in an automated manner at inference time, without requiring prior lesion annotations, manual initialisation, or region-of-interest pre-selection. We further investigate whether nodule localization benefits from the propagation of candidate regions across follow-up scans through inter-scan registration fields, as opposed to conventional independent per-scan analysis. The generalisability of the framework is systematically evaluated across four independent datasets. The results underscore the potential of automated pipelines for lung nodule analysis in screening and diagnostic settings.

Figures & tables

Figure 1: Overview of the proposed framework. For each CT scan $V_{i}$ , a Detection Module (DM) and a Classification Module (CM) generate a binary volumetric ROI $B_{i}$ for candidate nodule locations. Volumetric ROIs are then processed by a Transfer Module (TM) to produce a refined ROI $\widetilde{B_{i}}$ , which, together with the original CT scan $V_{i}$ , is passed to the Segmentation Module (SM) to obtain the nodule segmentation mask $L_{i}$ .

		CPM $\uparrow$				SEN (8FP/scan) $\uparrow$
Dataset	Selection	D-De	L-De	ENS	NNet [ 10 ]	D-De	L-De	ENS	NNet [ 10 ]
LongCT	all	14.6	15.8	19.0	38.0	21.2	24.8	26.6	66.9
LongCT	${\leq}6$ Ls/scan	23.1	21.8	26.9	46.1	30.3	33.9	36.4	71.3
UniToChest	all	18.1	22.5	25.9	28.1	22.5	28.0	30.3	47.6
UniToChest	${\leq}6$ Ls/scan	66.3	60.6	72.0	47.4	69.8	68.2	75.7	65.4
NLST-Sybil	all	67.6	48.0	70.9	44.7	76.1	63.6	78.9	76.9

Table 1: Average CPM (

\%

) and sensitivity at 8 FP/scan (SEN,

\%

) of the detection models (D-De, L-De, and ENS) on the test datasets, compared to NoduleNet (NNet). For Longitudinal-CT and UniToChest, results are also given for a subset of scans with less than 6 lesions (Ls/scan). Bold indicates best performance.

	DSC $\uparrow$				IoU $\uparrow$		Hit rate $\uparrow$
	LongCT		UniToChest		NLST-Sybil		NLST-LongCT
Model	$<1\;\text{cm}$	$\geq 1\;\text{cm}$	$<1\;\text{cm}$	$\geq 1\;\text{cm}$	$<1\;\text{cm}$	$\geq 1\;\text{cm}$	$<1\;\text{cm}$	$\geq 1\;\text{cm}$
TS [ 29 ]	58.8 $\pm$ 0.8	80.2 $\pm$ 0.9	54.4 $\pm$ 1.8	74.1 $\pm$ 1.6	13.7 $\pm$ 3.2	17.0 $\pm$ 1.3	78.0 $\pm$ 8.0	12.1 $\pm$ 3.8
NNet [ 10 ]	53.5 $\pm$ 0.9	56.1 $\pm$ 2.0	50.8 $\pm$ 2.1	59.4 $\pm$ 2.6	45.3 $\pm$ 4.5	33.6 $\pm$ 1.5	50.2 $\pm 6.1$	32.8 $\pm 8.2$
S0-B	73.4 $\pm$ 1.1	60.4 $\pm$ 2.1	66.1 $\pm$ 1.6	66.0 $\pm$ 2.1	45.9 $\pm$ 5.0	36.0 $\pm$ 1.7	91.0 $\pm$ 4.6	24.6 $\pm$ 5.5
S0-P	77.6 $\pm$ 0.8	79.3 $\pm$ 1.6	67.1 $\pm$ 1.8	77.4 $\pm$ 1.7	44.3 $\pm$ 4.0	33.6 $\pm$ 1.7	91.0 $\pm$ 4.6	22.5 $\pm$ 5.3

Table 2: Segmentation performance (DSC for LongCT and UniToChest; IoU for NLST-Sybil; hit rate for NLST-LongCT) on the test datasets, compared with TotalSegmentator (TS), NoduleNet (NNet) and MedSAM2 (MS2). The segmentation model got prompt annotations (points "P" or boxes "B") from the TM, merged according to (S0), (S1) or (S2) strategies. For the NLST-LongCT, S1 and S2 aggregation strategies were not applicable, as nodule annotations are available for only a single time point. Bold and underlined values denote the best and second-best performance, respectively.

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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+8

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Medical Image Segmentation Computed Tomography

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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.

Abstract

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