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Article ; Online: Imaging Appearance of Migraine and Tension Type Headache.

Mahammedi, Abdelkader / Wang, Lily L / Vagal, Achala S

2022 Volume 40, Issue 3, Page(s) 491–505

MeSH term(s)	Diagnostic Imaging ; Humans ; Migraine Disorders/diagnostic imaging ; Tension-Type Headache/diagnostic imaging
Language	English
Publishing date	2022-06-29
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	1013148-6
ISSN	1557-9875 ; 0733-8619
ISSN (online)	1557-9875
ISSN	0733-8619
DOI	10.1016/j.ncl.2022.02.002
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Article ; Online: Imaging of Headache Attributed to Vascular Disorders.

Wang, Lily L / Mahammedi, Abdelkader / Vagal, Achala S

Neurologic clinics

2022 Volume 40, Issue 3, Page(s) 507–530

Abstract: Imaging is essential in the diagnosis of vascular causes of headaches. With advances in technology, there are increasing options of imaging modalities to choose from, each with its own advantages and disadvantages. This article will focus on imaging ... ...

Abstract	Imaging is essential in the diagnosis of vascular causes of headaches. With advances in technology, there are increasing options of imaging modalities to choose from, each with its own advantages and disadvantages. This article will focus on imaging pearls and pitfalls of vascular causes of headaches. These include aneurysms, vasculitides, vascular malformations, and cerebral venous thrombosis.
MeSH term(s)	Headache/diagnostic imaging ; Headache/etiology ; Humans ; Magnetic Resonance Angiography ; Tomography, X-Ray Computed
Language	English
Publishing date	2022-06-29
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	1013148-6
ISSN	1557-9875 ; 0733-8619
ISSN (online)	1557-9875
ISSN	0733-8619
DOI	10.1016/j.ncl.2022.02.004
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Article: Lessons learned from evolving frameworks in adult glioblastoma.

Lechpammer, Mirna / Mahammedi, Abdelkader / Pomeranz Krummel, Daniel A / Sengupta, Soma

Handbook of clinical neurology

2023 Volume 192, Page(s) 131–140

Abstract: Glioblastoma (GBM) is the most common and aggressive malignant adult brain tumor. Significant effort has been directed to achieve a molecular subtyping of GBM to impact treatment. The discovery of new unique molecular alterations has resulted in a more ... ...

Abstract	Glioblastoma (GBM) is the most common and aggressive malignant adult brain tumor. Significant effort has been directed to achieve a molecular subtyping of GBM to impact treatment. The discovery of new unique molecular alterations has resulted in a more effective classification of tumors and has opened the door to subtype-specific therapeutic targets. Morphologically identical GBM may have different genetic, epigenetic, and transcriptomic alterations and therefore different progression trajectories and response to treatments. With a transition to molecularly guided diagnosis, there is now a potential to personalize and successfully manage this tumor type to improve outcomes. The steps to achieve subtype-specific molecular signatures can be extrapolated to other neuroproliferative as well as neurodegenerative disorders.
MeSH term(s)	Humans ; Adult ; Glioblastoma/genetics ; Glioblastoma/therapy ; Glioblastoma/pathology ; Brain Neoplasms/genetics ; Brain Neoplasms/therapy ; Brain Neoplasms/pathology ; Gene Expression Profiling/methods
Language	English
Publishing date	2023-01-06
Publishing country	Netherlands
Document type	Review ; Journal Article
ISSN	0072-9752
ISSN	0072-9752
DOI	10.1016/B978-0-323-85538-9.00011-0
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Article: Mixing Enhancement of Non-Newtonian Shear-Thinning Fluid for a Kenics Micromixer.

Mahammedi, Abdelkader / Tayeb, Naas Toufik / Kim, Kwang-Yong / Hossain, Shakhawat

Micromachines

2021 Volume 12, Issue 12

Abstract: In this work, a numerical investigation was analyzed to exhibit the mixing behaviors of non-Newtonian shear-thinning fluids in Kenics micromixers. The numerical analysis was performed using the computational fluid dynamic (CFD) tool to solve 3D Navier- ... ...

Abstract	In this work, a numerical investigation was analyzed to exhibit the mixing behaviors of non-Newtonian shear-thinning fluids in Kenics micromixers. The numerical analysis was performed using the computational fluid dynamic (CFD) tool to solve 3D Navier-Stokes equations with the species transport equations. The efficiency of mixing is estimated by the calculation of the mixing index for different cases of Reynolds number. The geometry of micro Kenics collected with a series of six helical elements twisted 180° and arranged alternately to achieve the higher level of chaotic mixing, inside a pipe with a Y-inlet. Under a wide range of Reynolds numbers between 0.1 to 500 and the carboxymethyl cellulose (CMC) solutions with power-law indices among 1 to 0.49, the micro-Kenics proves high mixing Performances at low and high Reynolds number. Moreover the pressure losses of the shear-thinning fluids for different Reynolds numbers was validated and represented.
Language	English
Publishing date	2021-11-30
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2620864-7
ISSN	2072-666X
ISSN	2072-666X
DOI	10.3390/mi12121494
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Article ; Online: Random expert sampling for deep learning segmentation of acute ischemic stroke on non-contrast CT.

Ostmeier, Sophie / Axelrod, Brian / Liu, Yongkai / Yu, Yannan / Jiang, Bin / Yuen, Nicole / Pulli, Benjamin / Verhaaren, Benjamin F J / Kaka, Hussam / Wintermark, Max / Michel, Patrik / Mahammedi, Abdelkader / Federau, Christian / Lansberg, Maarten G / Albers, Gregory W / Moseley, Michael E / Zaharchuk, Gregory / Heit, Jeremy J

Journal of neurointerventional surgery

2024

Abstract: Background: Outlining acutely infarcted tissue on non-contrast CT is a challenging task for which human inter-reader agreement is limited. We explored two different methods for training a supervised deep learning algorithm: one that used a segmentation ... ...

Abstract	Background: Outlining acutely infarcted tissue on non-contrast CT is a challenging task for which human inter-reader agreement is limited. We explored two different methods for training a supervised deep learning algorithm: one that used a segmentation defined by majority vote among experts and another that trained randomly on separate individual expert segmentations. Methods: The data set consisted of 260 non-contrast CT studies in 233 patients with acute ischemic stroke recruited from the multicenter DEFUSE 3 (Endovascular Therapy Following Imaging Evaluation for Ischemic Stroke 3) trial. Additional external validation was performed using 33 patients with matched stroke onset times from the University Hospital Lausanne. A benchmark U-Net was trained on the reference annotations of three experienced neuroradiologists to segment ischemic brain tissue using majority vote and random expert sampling training schemes. The median of volume, overlap, and distance segmentation metrics were determined for agreement in lesion segmentations between (1) three experts, (2) the majority model and each expert, and (3) the random model and each expert. The two sided Wilcoxon signed rank test was used to compare performances (1) to 2) and (1) to (3). We further compared volumes with the 24 hour follow-up diffusion weighted imaging (DWI, final infarct core) and correlations with clinical outcome (modified Rankin Scale (mRS) at 90 days) with the Spearman method. Results: The random model outperformed the inter-expert agreement ((1) to (2)) and the majority model ((1) to (3)) (dice 0.51±0.04 vs 0.36±0.05 (P Conclusion: The random model for ischemic injury delineation on non-contrast CT surpassed the inter-expert agreement ((1) to (2)) and the performance of the majority model ((1) to (3)). We showed that the random model volumetric measures of the model were consistent with 24 hour follow-up DWI.
Language	English
Publishing date	2024-02-01
Publishing country	England
Document type	Journal Article
ZDB-ID	2514982-9
ISSN	1759-8486 ; 1759-8478
ISSN (online)	1759-8486
ISSN	1759-8478
DOI	10.1136/jnis-2023-021283
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Article ; Online: USE-Evaluator: Performance metrics for medical image segmentation models supervised by uncertain, small or empty reference annotations in neuroimaging.

Ostmeier, Sophie / Axelrod, Brian / Isensee, Fabian / Bertels, Jeroen / Mlynash, Michael / Christensen, Soren / Lansberg, Maarten G / Albers, Gregory W / Sheth, Rajen / Verhaaren, Benjamin F J / Mahammedi, Abdelkader / Li, Li-Jia / Zaharchuk, Greg / Heit, Jeremy J

Medical image analysis

2023 Volume 90, Page(s) 102927

Abstract: Performance metrics for medical image segmentation models are used to measure the agreement between the reference annotation and the predicted segmentation. Usually, overlap metrics, such as the Dice, are used as a metric to evaluate the performance of ... ...

Abstract	Performance metrics for medical image segmentation models are used to measure the agreement between the reference annotation and the predicted segmentation. Usually, overlap metrics, such as the Dice, are used as a metric to evaluate the performance of these models in order for results to be comparable. However, there is a mismatch between the distributions of cases and the difficulty level of segmentation tasks in public data sets compared to clinical practice. Common metrics used to assess performance fail to capture the impact of this mismatch, particularly when dealing with datasets in clinical settings that involve challenging segmentation tasks, pathologies with low signal, and reference annotations that are uncertain, small, or empty. Limitations of common metrics may result in ineffective machine learning research in designing and optimizing models. To effectively evaluate the clinical value of such models, it is essential to consider factors such as the uncertainty associated with reference annotations, the ability to accurately measure performance regardless of the size of the reference annotation volume, and the classification of cases where reference annotations are empty. We study how uncertain, small, and empty reference annotations influence the value of metrics on a stroke in-house data set regardless of the model. We examine metrics behavior on the predictions of a standard deep learning framework in order to identify suitable metrics in such a setting. We compare our results to the BRATS 2019 and Spinal Cord public data sets. We show how uncertain, small, or empty reference annotations require a rethinking of the evaluation. The evaluation code was released to encourage further analysis of this topic https://github.com/SophieOstmeier/UncertainSmallEmpty.git.
Language	English
Publishing date	2023-08-10
Publishing country	Netherlands
Document type	Journal Article
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.2023.102927
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Article ; Online: Non-inferiority of deep learning ischemic stroke segmentation on non-contrast CT within 16-hours compared to expert neuroradiologists.

Ostmeier, Sophie / Axelrod, Brian / Verhaaren, Benjamin F J / Christensen, Soren / Mahammedi, Abdelkader / Liu, Yongkai / Pulli, Benjamin / Li, Li-Jia / Zaharchuk, Greg / Heit, Jeremy J

Scientific reports

2023 Volume 13, Issue 1, Page(s) 16153

Abstract: We determined if a convolutional neural network (CNN) deep learning model can accurately segment acute ischemic changes on non-contrast CT compared to neuroradiologists. Non-contrast CT (NCCT) examinations from 232 acute ischemic stroke patients who were ...

Abstract	We determined if a convolutional neural network (CNN) deep learning model can accurately segment acute ischemic changes on non-contrast CT compared to neuroradiologists. Non-contrast CT (NCCT) examinations from 232 acute ischemic stroke patients who were enrolled in the DEFUSE 3 trial were included in this study. Three experienced neuroradiologists independently segmented hypodensity that reflected the ischemic core on each scan. The neuroradiologist with the most experience (expert A) served as the ground truth for deep learning model training. Two additional neuroradiologists' (experts B and C) segmentations were used for data testing. The 232 studies were randomly split into training and test sets. The training set was further randomly divided into 5 folds with training and validation sets. A 3-dimensional CNN architecture was trained and optimized to predict the segmentations of expert A from NCCT. The performance of the model was assessed using a set of volume, overlap, and distance metrics using non-inferiority thresholds of 20%, 3 ml, and 3 mm, respectively. The optimized model trained on expert A was compared to test experts B and C. We used a one-sided Wilcoxon signed-rank test to test for the non-inferiority of the model-expert compared to the inter-expert agreement. The final model performance for the ischemic core segmentation task reached a performance of 0.46 ± 0.09 Surface Dice at Tolerance 5mm and 0.47 ± 0.13 Dice when trained on expert A. Compared to the two test neuroradiologists the model-expert agreement was non-inferior to the inter-expert agreement, [Formula: see text]. The before, CNN accurately delineates the hypodense ischemic core on NCCT in acute ischemic stroke patients with an accuracy comparable to neuroradiologists.
MeSH term(s)	Humans ; Ischemic Stroke/diagnostic imaging ; Deep Learning ; Neural Networks, Computer ; Radiologists ; Tomography, X-Ray Computed ; Stroke/diagnostic imaging
Language	English
Publishing date	2023-09-26
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-023-42961-x
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Article ; Online: Neurofibromatosis Type 2 (NF2) and the Implications for Vestibular Schwannoma and Meningioma Pathogenesis.

Bachir, Suha / Shah, Sanjit / Shapiro, Scott / Koehler, Abigail / Mahammedi, Abdelkader / Samy, Ravi N / Zuccarello, Mario / Schorry, Elizabeth / Sengupta, Soma

International journal of molecular sciences

2021 Volume 22, Issue 2

Abstract: Patients diagnosed with neurofibromatosis type 2 (NF2) are extremely likely to develop meningiomas, in addition to vestibular schwannomas. Meningiomas are a common primary brain tumor; many NF2 patients suffer from multiple meningiomas. In NF2, patients ... ...

Abstract	Patients diagnosed with neurofibromatosis type 2 (NF2) are extremely likely to develop meningiomas, in addition to vestibular schwannomas. Meningiomas are a common primary brain tumor; many NF2 patients suffer from multiple meningiomas. In NF2, patients have mutations in the
MeSH term(s)	Animals ; Apoptosis/genetics ; Brain Neoplasms/genetics ; Brain Neoplasms/pathology ; Cell Proliferation/genetics ; Humans ; Meningeal Neoplasms/genetics ; Meningeal Neoplasms/pathology ; Meningioma/genetics ; Meningioma/pathology ; Mutation/genetics ; Neurofibromatosis 2/genetics ; Neurofibromatosis 2/pathology ; Neuroma, Acoustic/genetics ; Neuroma, Acoustic/pathology
Language	English
Publishing date	2021-01-12
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2019364-6
ISSN	1422-0067 ; 1422-0067 ; 1661-6596
ISSN (online)	1422-0067
ISSN	1422-0067 ; 1661-6596
DOI	10.3390/ijms22020690
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Book ; Online: Random Expert Sampling for Deep Learning Segmentation of Acute Ischemic Stroke on Non-contrast CT

Ostmeier, Sophie / Axelrod, Brian / Pulli, Benjamin / Verhaaren, Benjamin F. J. / Mahammedi, Abdelkader / Liu, Yongkai / Federau, Christian / Zaharchuk, Greg / Heit, Jeremy J.

2023

Abstract: Purpose: Multi-expert deep learning training methods to automatically quantify ischemic brain tissue on Non-Contrast CT Materials and Methods: The data set consisted of 260 Non-Contrast CTs from 233 patients of acute ischemic stroke patients recruited in ...

Abstract	Purpose: Multi-expert deep learning training methods to automatically quantify ischemic brain tissue on Non-Contrast CT Materials and Methods: The data set consisted of 260 Non-Contrast CTs from 233 patients of acute ischemic stroke patients recruited in the DEFUSE 3 trial. A benchmark U-Net was trained on the reference annotations of three experienced neuroradiologists to segment ischemic brain tissue using majority vote and random expert sampling training schemes. We used a one-sided Wilcoxon signed-rank test on a set of segmentation metrics to compare bootstrapped point estimates of the training schemes with the inter-expert agreement and ratio of variance for consistency analysis. We further compare volumes with the 24h-follow-up DWI (final infarct core) in the patient subgroup with full reperfusion and we test volumes for correlation to the clinical outcome (mRS after 30 and 90 days) with the Spearman method. Results: Random expert sampling leads to a model that shows better agreement with experts than experts agree among themselves and better agreement than the agreement between experts and a majority-vote model performance (Surface Dice at Tolerance 5mm improvement of 61% to 0.70 +- 0.03 and Dice improvement of 25% to 0.50 +- 0.04). The model-based predicted volume similarly estimated the final infarct volume and correlated better to the clinical outcome than CT perfusion. Conclusion: A model trained on random expert sampling can identify the presence and location of acute ischemic brain tissue on Non-Contrast CT similar to CT perfusion and with better consistency than experts. This may further secure the selection of patients eligible for endovascular treatment in less specialized hospitals.
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	616
Publishing date	2023-09-07
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Pyogenic brain abscess, ventriculitis and diffuse meningitis with fatal outcome in an adult: Radiologic-pathologic correlation

Mahammedi, Abdelkader / Bachir, Suha / Purdy, Jenna / Larvie, Mykol / Buehler, Mark

Radiology case reports

2018 Volume 13, Issue 5, Page(s) 1063–1068

Abstract: Rupture of brain abscesses with evolution into ventriculitis with meningitis may result in sudden and dramatic worsening of the clinical situation. We present a 57-year-old man with such an event and fatal outcome. Multiple imaging modalities including ... ...

Abstract	Rupture of brain abscesses with evolution into ventriculitis with meningitis may result in sudden and dramatic worsening of the clinical situation. We present a 57-year-old man with such an event and fatal outcome. Multiple imaging modalities including computed tomography and advanced magnetic resonance imaging were correlated with gross specimen and histologic images. The differential diagnosis of multiple lesions with ring enhancement and prominent perifocal edema includes mainly infectious and neoplastic processes, such as brain abscess, metastasis, and multicentric glioblastoma. Pyogenic ventriculitis is an uncommon manifestation of severe intracranial infection that might be clinically obscure. We discuss the characteristic magnetic resonance findings of brain abscess and its complications, including meningitis and ventriculitis with emphasis on the role of diffusion-weighted and fluid-attenuated inversion recovery imaging.
Language	English
Publishing date	2018-05-18
Publishing country	Netherlands
Document type	Case Reports
ZDB-ID	2406300-9
ISSN	1930-0433
ISSN	1930-0433
DOI	10.1016/j.radcr.2018.04.019
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