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Article ; Online: Automatic Brand Identification of Orthopedic Implants from Radiographs: Ready for the Next Step?

Huisman, Merel / Lessmann, Nikolas

Radiology. Artificial intelligence

2022 Volume 4, Issue 2, Page(s) e220008

Language	English
Publishing date	2022-03-02
Publishing country	United States
Document type	Journal Article
ISSN	2638-6100
ISSN (online)	2638-6100
DOI	10.1148/ryai.220008
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: The effect of deep learning-based lesion segmentation on failure load calculations of metastatic femurs using finite element analysis.

Ataei, Ali / Eggermont, Florieke / Verdonschot, Nico / Lessmann, Nikolas / Tanck, Esther

Bone

2023 Volume 179, Page(s) 116987

Abstract: Bone ranks as the third most frequent tissue affected by cancer metastases, following the lung and liver. Bone metastases are often painful and may result in pathological fracture, which is a major cause of morbidity and mortality in cancer patients. To ... ...

Abstract	Bone ranks as the third most frequent tissue affected by cancer metastases, following the lung and liver. Bone metastases are often painful and may result in pathological fracture, which is a major cause of morbidity and mortality in cancer patients. To quantify fracture risk, finite element (FE) analysis has shown to be a promising tool, but metastatic lesions are typically not specifically segmented and therefore their mechanical properties may not be represented adequately. Deep learning methods potentially provide the opportunity to automatically segment these lesions and change the mechanical properties more adequately. In this study, our primary focus was to gain insight into the performance of an automatic segmentation algorithm for femoral metastatic lesions using deep learning methods and the subsequent effects on FE outcomes. The aims were to determine the similarity between manual segmentation and automatic segmentation; the differences in predicted failure load between FE models with automatically segmented osteolytic and mixed lesions and the models with CT-based lesion values (the gold standard); and the effect on the BOne Strength (BOS) score (failure load adjusted for body weight) and subsequent fracture risk assessments. From two patient cohorts, a total number of 50 femurs with osteolytic and mixed metastatic lesions were included in this study. The femurs were segmented from CT images and transferred into FE meshes. The material behavior was implemented as non-linear isotropic. These FE models were considered as gold standard (Finite Element no Segmented Lesion: FE-no-SL), whereby the local calcium equivalent density of both femur and metastatic lesion was extracted from CT-values. Lesions in the femur were manually segmented by two biomechanical experts after which final lesion segmentation for each femur was obtained based on consensus of opinions between two observers. Subsequently, a self-configuring variant of the popular deep learning model U-Net known as nnU-Net was used to automatically segment metastatic lesions within the femur. For these models with segmented lesions (Finite Element with Segmented Lesion: FE-with-SL), the calcium equivalent density within the metastatic lesions was set to zero after being segmented by the neural network, simulating absence of load-bearing capacity of these lesions. The models (either with or without automatically segmented lesions) were loaded incrementally in axial direction until failure was simulated. Dice coefficient was used to evaluate the similarity of the manual and automatic segmentation. Mean calcium equivalent density values within the automatically segmented lesions were calculated. Failure loads and patterns were determined. Furthermore, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for both groups by comparing the predictions to the occurrence or absence of actual fracture within the patient cohorts. The automatic segmentation algorithm performed in a none-robust manner. Dice coefficients describing the similarity between consented manual and automatic segmentations were relatively low (mean 0.45 ± standard deviation 0.33, median 0.54). Failure load difference between the FE-no-SL and FE-with-SL groups varied from 0 % to 48 % (mean 6.6 %). Correlation analysis of failure loads between the two groups showed a strong relationship (R
MeSH term(s)	Humans ; Finite Element Analysis ; Calcium ; Deep Learning ; Femur/diagnostic imaging ; Bone Neoplasms/diagnostic imaging ; Bone Neoplasms/secondary
Chemical Substances	Calcium (SY7Q814VUP)
Language	English
Publishing date	2023-12-05
Publishing country	United States
Document type	Journal Article
ZDB-ID	632515-4
ISSN	1873-2763 ; 8756-3282
ISSN (online)	1873-2763
ISSN	8756-3282
DOI	10.1016/j.bone.2023.116987
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Zs.A 1571: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (1.OG) ab Jg. 2022: Lesesaal (EG)
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Book ; Online: Random smooth gray value transformations for cross modality learning with gray value invariant networks

Lessmann, Nikolas / van Ginneken, Bram

2020

Abstract: Random transformations are commonly used for augmentation of the training data with the goal of reducing the uniformity of the training samples. These transformations normally aim at variations that can be expected in images from the same modality. Here, ...

Abstract	Random transformations are commonly used for augmentation of the training data with the goal of reducing the uniformity of the training samples. These transformations normally aim at variations that can be expected in images from the same modality. Here, we propose a simple method for transforming the gray values of an image with the goal of reducing cross modality differences. This approach enables segmentation of the lumbar vertebral bodies in CT images using a network trained exclusively with MR images. The source code is made available at https://github.com/nlessmann/rsgt
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition
Publishing date	2020-03-13
Publishing country	us
Document type	Book ; Online
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Article: Learning coronary artery calcium scoring in coronary CTA from non-contrast CT using unsupervised domain adaptation.

Zhai, Zhiwei / van Velzen, Sanne G M / Lessmann, Nikolas / Planken, Nils / Leiner, Tim / Išgum, Ivana

Frontiers in cardiovascular medicine

2022 Volume 9, Page(s) 981901

Abstract: Deep learning methods have demonstrated the ability to perform accurate coronary artery calcium (CAC) scoring. However, these methods require large and representative training data hampering applicability to diverse CT scans showing the heart and the ... ...

Abstract	Deep learning methods have demonstrated the ability to perform accurate coronary artery calcium (CAC) scoring. However, these methods require large and representative training data hampering applicability to diverse CT scans showing the heart and the coronary arteries. Training methods that accurately score CAC in cross-domain settings remains challenging. To address this, we present an unsupervised domain adaptation method that learns to perform CAC scoring in coronary CT angiography (CCTA) from non-contrast CT (NCCT). To address the domain shift between NCCT (source) domain and CCTA (target) domain, feature distributions are aligned between two domains using adversarial learning. A CAC scoring convolutional neural network is divided into a feature generator that maps input images to features in the latent space and a classifier that estimates predictions from the extracted features. For adversarial learning, a discriminator is used to distinguish the features between source and target domains. Hence, the feature generator aims to extract features with aligned distributions to fool the discriminator. The network is trained with adversarial loss as the objective function and a classification loss on the source domain as a constraint for adversarial learning. In the experiments, three data sets were used. The network is trained with 1,687 labeled chest NCCT scans from the National Lung Screening Trial. Furthermore, 200 labeled cardiac NCCT scans and 200 unlabeled CCTA scans were used to train the generator and the discriminator for unsupervised domain adaptation. Finally, a data set containing 313 manually labeled CCTA scans was used for testing. Directly applying the CAC scoring network trained on NCCT to CCTA led to a sensitivity of 0.41 and an average false positive volume 140 mm
Language	English
Publishing date	2022-09-12
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2781496-8
ISSN	2297-055X
ISSN	2297-055X
DOI	10.3389/fcvm.2022.981901
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Article ; Online: Lumbar spine segmentation in MR images: a dataset and a public benchmark.

van der Graaf, Jasper W / van Hooff, Miranda L / Buckens, Constantinus F M / Rutten, Matthieu / van Susante, Job L C / Kroeze, Robert Jan / de Kleuver, Marinus / van Ginneken, Bram / Lessmann, Nikolas

Scientific data

2024 Volume 11, Issue 1, Page(s) 264

Abstract: This paper presents a large publicly available multi-center lumbar spine magnetic resonance imaging (MRI) dataset with reference segmentations of vertebrae, intervertebral discs (IVDs), and spinal canal. The dataset includes 447 sagittal T1 and T2 MRI ... ...

Abstract	This paper presents a large publicly available multi-center lumbar spine magnetic resonance imaging (MRI) dataset with reference segmentations of vertebrae, intervertebral discs (IVDs), and spinal canal. The dataset includes 447 sagittal T1 and T2 MRI series from 218 patients with a history of low back pain and was collected from four different hospitals. An iterative data annotation approach was used by training a segmentation algorithm on a small part of the dataset, enabling semi-automatic segmentation of the remaining images. The algorithm provided an initial segmentation, which was subsequently reviewed, manually corrected, and added to the training data. We provide reference performance values for this baseline algorithm and nnU-Net, which performed comparably. Performance values were computed on a sequestered set of 39 studies with 97 series, which were additionally used to set up a continuous segmentation challenge that allows for a fair comparison of different segmentation algorithms. This study may encourage wider collaboration in the field of spine segmentation and improve the diagnostic value of lumbar spine MRI.
MeSH term(s)	Humans ; Algorithms ; Image Processing, Computer-Assisted/methods ; Intervertebral Disc/pathology ; Lumbar Vertebrae/diagnostic imaging ; Magnetic Resonance Imaging/methods ; Low Back Pain
Language	English
Publishing date	2024-03-02
Publishing country	England
Document type	Dataset ; Journal Article
ZDB-ID	2775191-0
ISSN	2052-4463 ; 2052-4463
ISSN (online)	2052-4463
ISSN	2052-4463
DOI	10.1038/s41597-024-03090-w
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Article ; Online: MRI image features with an evident relation to low back pain: a narrative review.

van der Graaf, Jasper W / Kroeze, Robert Jan / Buckens, Constantinus F M / Lessmann, Nikolas / van Hooff, Miranda L

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society

2023 Volume 32, Issue 5, Page(s) 1830–1841

Abstract: Purpose: Low back pain (LBP) is one of the most prevalent health condition worldwide and responsible for the most years lived with disability, yet the etiology is often unknown. Magnetic resonance imaging (MRI) is frequently used for treatment decision ... ...

Abstract	Purpose: Low back pain (LBP) is one of the most prevalent health condition worldwide and responsible for the most years lived with disability, yet the etiology is often unknown. Magnetic resonance imaging (MRI) is frequently used for treatment decision even though it is often inconclusive. There are many different image features that could relate to low back pain. Conversely, multiple etiologies do relate to spinal degeneration but do not actually cause the perceived pain. This narrative review provides an overview of all possible relevant features visible on MRI images and determines their relation to LBP. Methods: We conducted a separate literature search per image feature. All included studies were scored using the GRADE guidelines. Based on the reported results per feature an evidence agreement (EA) score was provided, enabling us to compare the collected evidence of separate image features. The various relations between MRI features and their associated pain mechanisms were evaluated to provide a list of features that are related to LBP. Results: All searches combined generated a total of 4472 hits of which 31 articles were included. Features were divided into five different categories:'discogenic', 'neuropathic','osseous', 'facetogenic', and'paraspinal', and discussed separately. Conclusion: Our research suggests that type I Modic changes, disc degeneration, endplate defects, disc herniation, spinal canal stenosis, nerve compression, and muscle fat infiltration have the highest probability to be related to LBP. These can be used to improve clinical decision-making for patients with LBP based on MRI.
MeSH term(s)	Humans ; Low Back Pain/diagnostic imaging ; Low Back Pain/etiology ; Low Back Pain/pathology ; Lumbar Vertebrae/pathology ; Intervertebral Disc Degeneration/complications ; Intervertebral Disc Degeneration/diagnostic imaging ; Intervertebral Disc Degeneration/pathology ; Intervertebral Disc Displacement/complications ; Magnetic Resonance Imaging/adverse effects
Language	English
Publishing date	2023-03-09
Publishing country	Germany
Document type	Journal Article ; Review ; Research Support, Non-U.S. Gov't
ZDB-ID	1115375-1
ISSN	1432-0932 ; 0940-6719
ISSN (online)	1432-0932
ISSN	0940-6719
DOI	10.1007/s00586-023-07602-x
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Zs.A 3437: Show issues

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bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (2.OG)
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Article ; Online: Development and validation of AI-based automatic measurement of coronal Cobb angles in degenerative scoliosis using sagittal lumbar MRI.

van der Graaf, Jasper W / van Hooff, Miranda L / van Ginneken, Bram / Huisman, Merel / Rutten, Matthieu / Lamers, Dominique / Lessmann, Nikolas / de Kleuver, Marinus

European radiology

2024

Abstract: Objectives: Severity of degenerative scoliosis (DS) is assessed by measuring the Cobb angle on anteroposterior radiographs. However, MRI images are often available to study the degenerative spine. This retrospective study aims to develop and evaluate ... ...

Abstract	Objectives: Severity of degenerative scoliosis (DS) is assessed by measuring the Cobb angle on anteroposterior radiographs. However, MRI images are often available to study the degenerative spine. This retrospective study aims to develop and evaluate the reliability of a novel automatic method that measures coronal Cobb angles on lumbar MRI in DS patients. Materials and methods: Vertebrae and intervertebral discs were automatically segmented using a 3D AI algorithm, trained on 447 lumbar MRI series. The segmentations were used to calculate all possible angles between the vertebral endplates, with the largest being the Cobb angle. The results were validated with 50 high-resolution sagittal lumbar MRI scans of DS patients, in which three experienced readers measured the Cobb angle. Reliability was determined using the intraclass correlation coefficient (ICC). Results: The ICCs between the readers ranged from 0.90 (95% CI 0.83-0.94) to 0.93 (95% CI 0.88-0.96). The ICC between the maximum angle found by the algorithm and the average manually measured Cobb angles was 0.83 (95% CI 0.71-0.90). In 9 out of the 50 cases (18%), all readers agreed on both vertebral levels for Cobb angle measurement. When using the algorithm to extract the angles at the vertebral levels chosen by the readers, the ICCs ranged from 0.92 (95% CI 0.87-0.96) to 0.97 (95% CI 0.94-0.98). Conclusion: The Cobb angle can be accurately measured on MRI using the newly developed algorithm in patients with DS. The readers failed to consistently choose the same vertebral level for Cobb angle measurement, whereas the automatic approach ensures the maximum angle is consistently measured. Clinical relevance statement: Our AI-based algorithm offers reliable Cobb angle measurement on routine MRI for degenerative scoliosis patients, potentially reducing the reliance on conventional radiographs, ensuring consistent assessments, and therefore improving patient care. Key points: • While often available, MRI images are rarely utilized to determine the severity of degenerative scoliosis. • The presented MRI Cobb angle algorithm is more reliable than humans in patients with degenerative scoliosis. • Radiographic imaging for Cobb angle measurements is mitigated when lumbar MRI images are available.
Language	English
Publishing date	2024-02-21
Publishing country	Germany
Document type	Journal Article
ZDB-ID	1085366-2
ISSN	1432-1084 ; 0938-7994 ; 1613-3749
ISSN (online)	1432-1084
ISSN	0938-7994 ; 1613-3749
DOI	10.1007/s00330-024-10616-8
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Book ; Online: Transfer learning from a sparsely annotated dataset of 3D medical images

Humpire-Mamani, Gabriel Efrain / Jacobs, Colin / Prokop, Mathias / van Ginneken, Bram / Lessmann, Nikolas

2023

Abstract: Transfer learning leverages pre-trained model features from a large dataset to save time and resources when training new models for various tasks, potentially enhancing performance. Due to the lack of large datasets in the medical imaging domain, ... ...

Abstract	Transfer learning leverages pre-trained model features from a large dataset to save time and resources when training new models for various tasks, potentially enhancing performance. Due to the lack of large datasets in the medical imaging domain, transfer learning from one medical imaging model to other medical imaging models has not been widely explored. This study explores the use of transfer learning to improve the performance of deep convolutional neural networks for organ segmentation in medical imaging. A base segmentation model (3D U-Net) was trained on a large and sparsely annotated dataset; its weights were used for transfer learning on four new down-stream segmentation tasks for which a fully annotated dataset was available. We analyzed the training set size's influence to simulate scarce data. The results showed that transfer learning from the base model was beneficial when small datasets were available, providing significant performance improvements; where fine-tuning the base model is more beneficial than updating all the network weights with vanilla transfer learning. Transfer learning with fine-tuning increased the performance by up to 0.129 (+28\%) Dice score than experiments trained from scratch, and on average 23 experiments increased the performance by 0.029 Dice score in the new segmentation tasks. The study also showed that cross-modality transfer learning using CT scans was beneficial. The findings of this study demonstrate the potential of transfer learning to improve the efficiency of annotation and increase the accessibility of accurate organ segmentation in medical imaging, ultimately leading to improved patient care. We made the network definition and weights publicly available to benefit other users and researchers.
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Artificial Intelligence ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
Subject code	006
Publishing date	2023-11-08
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Deep Learning-Quantified Calcium Scores for Automatic Cardiovascular Mortality Prediction at Lung Screening Low-Dose CT.

de Vos, Bob D / Lessmann, Nikolas / de Jong, Pim A / Išgum, Ivana

Radiology. Cardiothoracic imaging

2021 Volume 3, Issue 2, Page(s) e190219

Abstract: Purpose: To examine the prognostic value of location-specific arterial calcification quantities at lung screening low-dose CT for the prediction of cardiovascular disease (CVD) mortality.: Materials and methods: This retrospective study included 5564 ...

Abstract	Purpose: To examine the prognostic value of location-specific arterial calcification quantities at lung screening low-dose CT for the prediction of cardiovascular disease (CVD) mortality. Materials and methods: This retrospective study included 5564 participants who underwent low-dose CT from the National Lung Screening Trial between August 2002 and April 2004, who were followed until December 2009. A deep learning network was trained to quantify six types of vascular calcification: thoracic aorta calcification (TAC); aortic and mitral valve calcification; and coronary artery calcification (CAC) of the left main, the left anterior descending, and the right coronary artery. TAC and CAC were determined in six evenly distributed slabs spatially aligned among chest CT images. CVD mortality prediction was performed with multivariable logistic regression using least absolute shrinkage and selection operator. The methods were compared with semiautomatic baseline prediction using self-reported participant characteristics, such as age, history of smoking, and history of illness. Statistical significance between the prediction models was tested using the nonparametric DeLong test. Results: The prediction model was trained with data from 4451 participants (median age, 61 years; 37.9% women) and then tested on data from 1113 participants (median age, 61 years; 37.9% women). The prediction model using calcium scores achieved a C statistic of 0.74 (95% CI: 0.69, 0.79), and it outperformed the baseline model using only participant characteristics (C statistic, 0.69; Conclusion: Five-year CVD mortality prediction using automatically extracted image-based features is feasible at lung screening low-dose CT.© RSNA, 2021.
Language	English
Publishing date	2021-04-15
Publishing country	United States
Document type	Journal Article
ISSN	2638-6135
ISSN (online)	2638-6135
DOI	10.1148/ryct.2021190219
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Article ; Online: CNN-based lung CT registration with multiple anatomical constraints.

Hering, Alessa / Häger, Stephanie / Moltz, Jan / Lessmann, Nikolas / Heldmann, Stefan / van Ginneken, Bram

Medical image analysis

2021 Volume 72, Page(s) 102139

Abstract: Deep-learning-based registration methods emerged as a fast alternative to conventional registration methods. However, these methods often still cannot achieve the same performance as conventional registration methods because they are either limited to ... ...

Abstract	Deep-learning-based registration methods emerged as a fast alternative to conventional registration methods. However, these methods often still cannot achieve the same performance as conventional registration methods because they are either limited to small deformation or they fail to handle a superposition of large and small deformations without producing implausible deformation fields with foldings inside. In this paper, we identify important strategies of conventional registration methods for lung registration and successfully developed the deep-learning counterpart. We employ a Gaussian-pyramid-based multilevel framework that can solve the image registration optimization in a coarse-to-fine fashion. Furthermore, we prevent foldings of the deformation field and restrict the determinant of the Jacobian to physiologically meaningful values by combining a volume change penalty with a curvature regularizer in the loss function. Keypoint correspondences are integrated to focus on the alignment of smaller structures. We perform an extensive evaluation to assess the accuracy, the robustness, the plausibility of the estimated deformation fields, and the transferability of our registration approach. We show that it achieves state-of-the-art results on the COPDGene dataset compared to conventional registration method with much shorter execution time. In our experiments on the DIRLab exhale to inhale lung registration, we demonstrate substantial improvements (TRE below 1.2 mm) over other deep learning methods. Our algorithm is publicly available at https://grand-challenge.org/algorithms/deep-learning-based-ct-lung-registration/.
MeSH term(s)	Algorithms ; Humans ; Image Processing, Computer-Assisted ; Lung/diagnostic imaging ; Thorax ; Tomography, X-Ray Computed
Language	English
Publishing date	2021-06-22
Publishing country	Netherlands
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1356436-5
ISSN	1361-8423 ; 1361-8431 ; 1361-8415
ISSN (online)	1361-8423 ; 1361-8431
ISSN	1361-8415
DOI	10.1016/j.media.2021.102139
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