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Dwivedi K, Sharkey M, Alabed S, Langlotz CP, Swift AJ, Bluethgen C. External validation, radiological evaluation, and development of deep learning automatic lung segmentation in contrast-enhanced chest CT. Eur Radiol 2024; 34:2727-2737. [PMID: 37775589 PMCID: PMC10957646 DOI: 10.1007/s00330-023-10235-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/25/2023] [Accepted: 07/24/2023] [Indexed: 10/01/2023]
Abstract
OBJECTIVES There is a need for CT pulmonary angiography (CTPA) lung segmentation models. Clinical translation requires radiological evaluation of model outputs, understanding of limitations, and identification of failure points. This multicentre study aims to develop an accurate CTPA lung segmentation model, with evaluation of outputs in two diverse patient cohorts with pulmonary hypertension (PH) and interstitial lung disease (ILD). METHODS This retrospective study develops an nnU-Net-based segmentation model using data from two specialist centres (UK and USA). Model was trained (n = 37), tested (n = 12), and clinically evaluated (n = 176) on a diverse 'real-world' cohort of 225 PH patients with volumetric CTPAs. Dice score coefficient (DSC) and normalised surface distance (NSD) were used for testing. Clinical evaluation of outputs was performed by two radiologists who assessed clinical significance of errors. External validation was performed on heterogenous contrast and non-contrast scans from 28 ILD patients. RESULTS A total of 225 PH and 28 ILD patients with diverse demographic and clinical characteristics were evaluated. Mean accuracy, DSC, and NSD scores were 0.998 (95% CI 0.9976, 0.9989), 0.990 (0.9840, 0.9962), and 0.983 (0.9686, 0.9972) respectively. There were no segmentation failures. On radiological review, 82% and 71% of internal and external cases respectively had no errors. Eighteen percent and 25% respectively had clinically insignificant errors. Peripheral atelectasis and consolidation were common causes for suboptimal segmentation. One external case (0.5%) with patulous oesophagus had a clinically significant error. CONCLUSION State-of-the-art CTPA lung segmentation model provides accurate outputs with minimal clinical errors on evaluation across two diverse cohorts with PH and ILD. CLINICAL RELEVANCE Clinical translation of artificial intelligence models requires radiological review and understanding of model limitations. This study develops an externally validated state-of-the-art model with robust radiological review. Intended clinical use is in techniques such as lung volume or parenchymal disease quantification. KEY POINTS • Accurate, externally validated CT pulmonary angiography (CTPA) lung segmentation model tested in two large heterogeneous clinical cohorts (pulmonary hypertension and interstitial lung disease). • No segmentation failures and robust review of model outputs by radiologists found 1 (0.5%) clinically significant segmentation error. • Intended clinical use of this model is a necessary step in techniques such as lung volume, parenchymal disease quantification, or pulmonary vessel analysis.
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Affiliation(s)
- Krit Dwivedi
- Department of Infection, Immunity & Cardiovascular Disease, Medical School, University of Sheffield, Sheffield, UK.
- Academic Department of Radiology, Royal Hallamshire Hospital, Glossop Road, Sheffield, S10 2JF, USA.
| | - Michael Sharkey
- 3DLab, Sheffield Teaching Hospitals NHS Trust, Sheffield, UK
| | - Samer Alabed
- Department of Infection, Immunity & Cardiovascular Disease, Medical School, University of Sheffield, Sheffield, UK
| | - Curtis P Langlotz
- Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI), Stanford University, Sheffield, USA
| | - Andy J Swift
- Department of Infection, Immunity & Cardiovascular Disease, Medical School, University of Sheffield, Sheffield, UK
| | - Christian Bluethgen
- Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI), Stanford University, Sheffield, USA
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Mulliez D, Poncelet E, Ferret L, Hoeffel C, Hamet B, Dang LA, Laurent N, Ramette G. Three-Dimensional Measurement of the Uterus on Magnetic Resonance Images: Development and Performance Analysis of an Automated Deep-Learning Tool. Diagnostics (Basel) 2023; 13:2662. [PMID: 37627920 PMCID: PMC10453745 DOI: 10.3390/diagnostics13162662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 08/08/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023] Open
Abstract
Uterus measurements are useful for assessing both the treatment and follow-ups of gynaecological patients. The aim of our study was to develop a deep learning (DL) tool for fully automated measurement of the three-dimensional size of the uterus on magnetic resonance imaging (MRI). In this single-centre retrospective study, 900 cases were included to train, validate, and test a VGG-16/VGG-11 convolutional neural network (CNN). The ground truth was manual measurement. The performance of the model was evaluated using the objective key point similarity (OKS), the mean difference in millimetres, and coefficient of determination R2. The OKS of our model was 0.92 (validation) and 0.96 (test). The average deviation and R2 coefficient between the AI measurements and the manual ones were, respectively, 3.9 mm and 0.93 for two-point length, 3.7 mm and 0.94 for three-point length, 2.6 mm and 0.93 for width, 4.2 mm and 0.75 for thickness. The inter-radiologist variability was 1.4 mm. A three-dimensional automated measurement was obtained in 1.6 s. In conclusion, our model was able to locate the uterus on MRIs and place measurement points on it to obtain its three-dimensional measurement with a very good correlation compared to manual measurements.
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Affiliation(s)
- Daphné Mulliez
- Service d’Imagerie de la Femme, Centre Hospitalier de Valenciennes, 59300 Valenciennes, France; (E.P.); (B.H.); (L.A.D.); (N.L.); (G.R.)
| | - Edouard Poncelet
- Service d’Imagerie de la Femme, Centre Hospitalier de Valenciennes, 59300 Valenciennes, France; (E.P.); (B.H.); (L.A.D.); (N.L.); (G.R.)
| | - Laurie Ferret
- Unité de Recherche Clinique, Centre Hospitalier de Valenciennes, 59300 Valenciennes, France;
| | - Christine Hoeffel
- Service de Radiologie, Hôpital Maison Blanche, Avenue du Général Koenig, 51092 Reims, France;
| | - Blandine Hamet
- Service d’Imagerie de la Femme, Centre Hospitalier de Valenciennes, 59300 Valenciennes, France; (E.P.); (B.H.); (L.A.D.); (N.L.); (G.R.)
| | - Lan Anh Dang
- Service d’Imagerie de la Femme, Centre Hospitalier de Valenciennes, 59300 Valenciennes, France; (E.P.); (B.H.); (L.A.D.); (N.L.); (G.R.)
| | - Nicolas Laurent
- Service d’Imagerie de la Femme, Centre Hospitalier de Valenciennes, 59300 Valenciennes, France; (E.P.); (B.H.); (L.A.D.); (N.L.); (G.R.)
| | - Guillaume Ramette
- Service d’Imagerie de la Femme, Centre Hospitalier de Valenciennes, 59300 Valenciennes, France; (E.P.); (B.H.); (L.A.D.); (N.L.); (G.R.)
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Brown KH, Illyuk J, Ghita M, Walls GM, McGarry CK, Butterworth KT. Assessment of Variabilities in Lung-Contouring Methods on CBCT Preclinical Radiomics Outputs. Cancers (Basel) 2023; 15:2677. [PMCID: PMC10216427 DOI: 10.3390/cancers15102677] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 04/28/2023] [Accepted: 05/08/2023] [Indexed: 06/01/2023] Open
Abstract
Simple Summary This study is the first to evaluate the impact of contouring differences on radiomics analysis in preclinical CBCT scans. We found that the variation in quantitative image readouts was greater between segmentation tools than between observers. Abstract Radiomics image analysis has the potential to uncover disease characteristics for the development of predictive signatures and personalised radiotherapy treatment. Inter-observer and inter-software delineation variabilities are known to have downstream effects on radiomics features, reducing the reliability of the analysis. The purpose of this study was to investigate the impact of these variabilities on radiomics outputs from preclinical cone-beam computed tomography (CBCT) scans. Inter-observer variabilities were assessed using manual and semi-automated contours of mouse lungs (n = 16). Inter-software variabilities were determined between two tools (3D Slicer and ITK-SNAP). The contours were compared using Dice similarity coefficient (DSC) scores and the 95th percentile of the Hausdorff distance (HD95p) metrics. The good reliability of the radiomics outputs was defined using intraclass correlation coefficients (ICC) and their 95% confidence intervals. The median DSC scores were high (0.82–0.94), and the HD95p metrics were within the submillimetre range for all comparisons. the shape and NGTDM features were impacted the most. Manual contours had the most reliable features (73%), followed by semi-automated (66%) and inter-software (51%) variabilities. From a total of 842 features, 314 robust features overlapped across all contouring methodologies. In addition, our results have a 70% overlap with features identified from clinical inter-observer studies.
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Affiliation(s)
- Kathryn H. Brown
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK (M.G.); (G.M.W.); (C.K.M.); (K.T.B.)
| | - Jacob Illyuk
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK (M.G.); (G.M.W.); (C.K.M.); (K.T.B.)
| | - Mihaela Ghita
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK (M.G.); (G.M.W.); (C.K.M.); (K.T.B.)
| | - Gerard M. Walls
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK (M.G.); (G.M.W.); (C.K.M.); (K.T.B.)
- Northern Ireland Cancer Centre, Belfast Health & Social Care Trust, Belfast BT9 7JL, UK
| | - Conor K. McGarry
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK (M.G.); (G.M.W.); (C.K.M.); (K.T.B.)
- Northern Ireland Cancer Centre, Belfast Health & Social Care Trust, Belfast BT9 7JL, UK
| | - Karl T. Butterworth
- Patrick G. Johnston Centre for Cancer Research, Queen’s University Belfast, Belfast BT9 7AE, UK (M.G.); (G.M.W.); (C.K.M.); (K.T.B.)
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Ferl GZ, Barck KH, Patil J, Jemaa S, Malamut EJ, Lima A, Long JE, Cheng JH, Junttila MR, Carano RA. Automated segmentation of lungs and lung tumors in mouse micro-CT scans. iScience 2022; 25:105712. [PMID: 36582483 PMCID: PMC9792881 DOI: 10.1016/j.isci.2022.105712] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 10/28/2022] [Accepted: 11/29/2022] [Indexed: 12/12/2022] Open
Abstract
Here, we have developed an automated image processing algorithm for segmenting lungs and individual lung tumors in in vivo micro-computed tomography (micro-CT) scans of mouse models of non-small cell lung cancer and lung fibrosis. Over 3000 scans acquired across multiple studies were used to train/validate a 3D U-net lung segmentation model and a Support Vector Machine (SVM) classifier to segment individual lung tumors. The U-net lung segmentation algorithm can be used to estimate changes in soft tissue volume within lungs (primarily tumors and blood vessels), whereas the trained SVM is able to discriminate between tumors and blood vessels and identify individual tumors. The trained segmentation algorithms (1) significantly reduce time required for lung and tumor segmentation, (2) reduce bias and error associated with manual image segmentation, and (3) facilitate identification of individual lung tumors and objective assessment of changes in lung and individual tumor volumes under different experimental conditions.
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Affiliation(s)
- Gregory Z. Ferl
- Preclinical & Translational PKPD, Genentech, South San Francisco, CA 94080, USA,Department of Translational Imaging, Genentech, South San Francisco, CA 94080, USA,Corresponding author
| | - Kai H. Barck
- Department of Translational Imaging, Genentech, South San Francisco, CA 94080, USA,Corresponding author
| | - Jasmine Patil
- Genetic Science Group, Thermo Fisher Scientific, South San Francisco, CA 94080, USA
| | - Skander Jemaa
- Data, Analytics and Imaging, Product Development, Genentech, South San Francisco, CA 94080, USA
| | - Evelyn J. Malamut
- Preclinical & Translational PKPD, Genentech, South San Francisco, CA 94080, USA
| | - Anthony Lima
- Department of Translational Oncology, Genentech, South San Francisco, CA 94080, USA
| | - Jason E. Long
- ORIC Pharmaceuticals, South San Francisco, CA 94080, USA
| | - Jason H. Cheng
- Department of Translational Oncology, Genentech, South San Francisco, CA 94080, USA
| | | | - Richard A.D. Carano
- Data, Analytics and Imaging, Product Development, Genentech, South San Francisco, CA 94080, USA
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Vincenzi E, Fantazzini A, Basso C, Barla A, Odone F, Leo L, Mecozzi L, Mambrini M, Ferrini E, Sverzellati N, Stellari FF. A fully automated deep learning pipeline for micro-CT-imaging-based densitometry of lung fibrosis murine models. Respir Res 2022; 23:308. [DOI: 10.1186/s12931-022-02236-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 10/15/2022] [Indexed: 11/13/2022] Open
Abstract
AbstractIdiopathic pulmonary fibrosis, the archetype of pulmonary fibrosis (PF), is a chronic lung disease of a poor prognosis, characterized by progressively worsening of lung function. Although histology is still the gold standard for PF assessment in preclinical practice, histological data typically involve less than 1% of total lung volume and are not amenable to longitudinal studies. A miniaturized version of computed tomography (µCT) has been introduced to radiologically examine lung in preclinical murine models of PF. The linear relationship between X-ray attenuation and tissue density allows lung densitometry on total lung volume. However, the huge density changes caused by PF usually require manual segmentation by trained operators, limiting µCT deployment in preclinical routine. Deep learning approaches have achieved state-of-the-art performance in medical image segmentation. In this work, we propose a fully automated deep learning approach to segment right and left lung on µCT imaging and subsequently derive lung densitometry. Our pipeline first employs a convolutional network (CNN) for pre-processing at low-resolution and then a 2.5D CNN for higher-resolution segmentation, combining computational advantage of 2D and ability to address 3D spatial coherence without compromising accuracy. Finally, lungs are divided into compartments based on air content assessed by density. We validated this pipeline on 72 mice with different grades of PF, achieving a Dice score of 0.967 on test set. Our tests demonstrate that this automated tool allows for rapid and comprehensive analysis of µCT scans of PF murine models, thus laying the ground for its wider exploitation in preclinical settings.
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