scholarly journals NIMG-40. ROBUST MODALITY-AGNOSTIC SKULL-STRIPPING IN PRESENCE OF DIFFUSE GLIOMA: A MULTI-INSTITUTIONAL STUDY

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi170-vi170 ◽  
Author(s):  
Siddhesh Thakur ◽  
Jimit Doshi ◽  
Sung Min Ha ◽  
Gaurav Shukla ◽  
Aikaterini Kotrotsou ◽  
...  
Keyword(s):  
Human Performance ◽  
Ground Truth ◽  
Accurate Solution ◽  
Test Subject ◽  
Slice Thickness ◽  
Diffuse Glioma ◽  
Similarity Coefficients ◽  
Unseen Data ◽  
Skull Stripping ◽  
Mri Scans

Abstract BACKGROUND Skull-stripping describes essential pre-processing in neuro-imaging, directly impacting subsequent analyses. Existing skull-stripping algorithms are typically developed and validated only on T1-weighted MRI scans without apparent gliomas, hence may fail when applied on neuro-oncology scans. Furthermore, most algorithms have large computational footprint and lack generalization to different acquisition protocols, limiting their clinical use. We sought to identify a practical, generalizable, robust, and accurate solution to address all these limitations. METHODS We identified multi-institutional retrospective cohorts, describing pre-operative multi-parametric MRI modalities (T1,T1Gd,T2,T2-FLAIR) with distinct acquisition protocols (e.g., slice thickness, magnet strength), varying pre-applied image-based defacing techniques, and corresponding manually-delineated ground-truth brain masks. We developed a 3D fully convolutional deep learning architecture (3D-ResUNet). Following modality co-registration to a common anatomical template, the 3D-ResUNet was trained on 314 subjects from the University of Pennsylvania (UPenn), and evaluated on 91, 152, 25, and 29 unseen subjects from UPenn, Thomas Jefferson University (TJU), Washington University (WashU), and MD Anderson (MDACC), respectively. To achieve robustness against scanner/resolution variability and utilize all modalities, we introduced a novel “modality-agnostic” training approach, which allows application of the trained model on any single modality, without requiring a pre-determined modality as input. We calculate the final brain mask for any test subject by applying our trained modality-agnostic 3D-ResUNet model on the modality with the highest resolution. RESULTS The average(±stdDev) dice similarity coefficients achieved for our novel modality-agnostic model were equal to 97.81%+0.8, 95.59%+2.0, 91.61%+1.9, and 96.05%+1.4 for the unseen data from UPenn, TJU, WashU, and MDACC, respectively. CONCLUSIONS Our novel modality-agnostic skull-stripping approach produces robust near-human performance, generalizes across acquisition protocols, image-based defacing techniques, without requiring pre-determined input modalities or depending on the availability of a specific modality. Such an approach can facilitate tool standardization for harmonized pre-processing of neuro-oncology scans for multi-institutional collaborations, enabling further data sharing and computational analyses.

scholarly journals A detailed analysis of anatomical plausibility of crossed and uncrossed streamline rendition of the dentato-rubro-thalamic tract (DRT(T)) in a commercial stereotactic planning system

2021 ◽  
Author(s):  
Volker A. Coenen ◽  
Bastian E. Sajonz ◽  
Peter C. Reinacher ◽  
Christoph P. Kaller ◽  
Horst Urbach ◽  
...  
Keyword(s):  
Surgical Planning ◽  
Ground Truth ◽  
Planning System ◽  
Single Subject ◽  
Fiber Structure ◽  
Native Space ◽  
Volume Of Interest ◽  
Mri Scans ◽  
Magnetic Resonance Imaging Mri ◽  
The Individual

Abstract Background An increasing number of neurosurgeons use display of the dentato-rubro-thalamic tract (DRT) based on diffusion weighted imaging (dMRI) as basis for their routine planning of stimulation or lesioning approaches in stereotactic tremor surgery. An evaluation of the anatomical validity of the display of the DRT with respect to modern stereotactic planning systems and across different tracking environments has not been performed. Methods Distinct dMRI and anatomical magnetic resonance imaging (MRI) data of high and low quality from 9 subjects were used. Six subjects had repeated MRI scans and therefore entered the analysis twice. Standardized DICOM structure templates for volume of interest definition were applied in native space for all investigations. For tracking BrainLab Elements (BrainLab, Munich, Germany), two tensor deterministic tracking (FT2), MRtrix IFOD2 (https://www.mrtrix.org), and a global tracking (GT) approach were used to compare the display of the uncrossed (DRTu) and crossed (DRTx) fiber structure after transformation into MNI space. The resulting streamlines were investigated for congruence, reproducibility, anatomical validity, and penetration of anatomical way point structures. Results In general, the DRTu can be depicted with good quality (as judged by waypoints). FT2 (surgical) and GT (neuroscientific) show high congruence. While GT shows partly reproducible results for DRTx, the crossed pathway cannot be reliably reconstructed with the other (iFOD2 and FT2) algorithms. Conclusion Since a direct anatomical comparison is difficult in the individual subjects, we chose a comparison with two research tracking environments as the best possible “ground truth.” FT2 is useful especially because of its manual editing possibilities of cutting erroneous fibers on the single subject level. An uncertainty of 2 mm as mean displacement of DRTu is expectable and should be respected when using this approach for surgical planning. Tractographic renditions of the DRTx on the single subject level seem to be still illusive.

scholarly journals Improved PET/MRI attenuation correction in the pelvic region using a statistical decomposition method on T2-weighted images

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Elin Wallstén ◽  
Jan Axelsson ◽  
Joakim Jonsson ◽  
Camilla Thellenberg Karlsson ◽  
Tufve Nyholm ◽  
...  
Keyword(s):  
Attenuation Correction ◽  
Research Study ◽  
Standardized Uptake Value ◽  
Ground Truth ◽  
Whole Body ◽  
Mr Images ◽  
Pelvic Region ◽  
Mri Scans ◽  
The Mean ◽  
Prostate Cancer Research

Abstract Background Attenuation correction of PET/MRI is a remaining problem for whole-body PET/MRI. The statistical decomposition algorithm (SDA) is a probabilistic atlas-based method that calculates synthetic CTs from T2-weighted MRI scans. In this study, we evaluated the application of SDA for attenuation correction of PET images in the pelvic region. Materials and method Twelve patients were retrospectively selected from an ongoing prostate cancer research study. The patients had same-day scans of [11C]acetate PET/MRI and CT. The CT images were non-rigidly registered to the PET/MRI geometry, and PET images were reconstructed with attenuation correction employing CT, SDA-generated CT, and the built-in Dixon sequence-based method of the scanner. The PET images reconstructed using CT-based attenuation correction were used as ground truth. Results The mean whole-image PET uptake error was reduced from − 5.4% for Dixon-PET to − 0.9% for SDA-PET. The prostate standardized uptake value (SUV) quantification error was significantly reduced from − 5.6% for Dixon-PET to − 2.3% for SDA-PET. Conclusion Attenuation correction with SDA improves quantification of PET/MR images in the pelvic region compared to the Dixon-based method.

scholarly journals Inter-rater and inter-session reliability of lumbar paraspinal muscle composition in a mobile MRI device

2021 ◽  
pp. 20210141
Author(s):  
Anne Schomöller ◽  
Lucie Risch ◽  
Hannes Kaplick ◽  
Monique Wochatz ◽  
Tilman Engel ◽  
...  
Keyword(s):  
Spin Echo ◽  
Intraclass Correlation ◽  
Relaxation Times ◽  
Lumbar Vertebrae ◽  
Erector Spinae ◽  
Paraspinal Muscle ◽  
Slice Thickness ◽  
Systematic Bias ◽  
Reliable Assessment ◽  
Mri Scans

Objective: To assess the reliability of measurements of paraspinal muscle transverse relaxation times (T2 times) between two observers and within one observer on different time points. Methods: 14 participants (9f/5m, 33 ± 5 years, 176 ± 10 cm, 73 ± 12 kg) underwent 2 consecutive MRI scans (M1,M2) on the same day, followed by 1 MRI scan 13–14 days later (M3) in a mobile 1.5 Tesla MRI. T2 times were calculated in T2 weighted turbo spin-echo-sequences at the spinal level of the third lumbar vertebrae (11 slices, 2 mm slice thickness, 1 mm interslice gap, echo times: 20, 40, 60, 80, 100 ms) for M. erector spinae (ES) and M. multifidius (MF). The following reliability parameter were calculated for the agreement of T2 times between two different investigators (OBS1 & OBS2) on the same MRI (inter-rater reliability, IR) and by one investigator between different MRI of the same participant (intersession variability, IS): Test–Retest Variability (TRV, Differences/Mean*100); Coefficient of Variation (CV, Standard deviation/Mean*100); Bland–Altman Analysis (systematic bias = Mean of the Differences; Upper/Lower Limits of Agreement = Bias+/−1.96*SD); Intraclass Correlation Coefficient 3.1 (ICC) with absolute agreement, as well as its 95% confidence interval. Results: Mean TRV for IR was 2.6% for ES and 4.2% for MF. Mean TRV for IS was 3.5% (ES) and 5.1% (MF). Mean CV for IR was 1.9 (ES) and 3.0 (MF). Mean CV for IS was 2.5% (ES) and 3.6% (MF). A systematic bias of 1.3 ms (ES) and 2.1 ms (MF) were detected for IR and a systematic bias of 0.4 ms (ES) and 0.07 ms (MF) for IS. ICC for IR was 0.94 (ES) and 0.87 (MF). ICC for IS was 0.88 (ES) and 0.82 (MF). Conclusion: Reliable assessment of paraspinal muscle T2 time justifies its use for scientific purposes. The applied technique could be recommended to use for future studies that aim to assess changes of T2 times, e.g. after an intense bout of eccentric exercises.

scholarly journals Skull-Stripping of Glioblastoma MRI Scans Using 3D Deep Learning

2020 ◽  
pp. 57-68
Author(s):  
Siddhesh P. Thakur ◽  
Jimit Doshi ◽  
Sarthak Pati ◽  
Sung Min Ha ◽  
Chiharu Sako ◽  
...  
Keyword(s):  
Deep Learning ◽  
Skull Stripping ◽  
Mri Scans

scholarly journals A deep learning toolbox for noise-optimized, generalized spike inference from calcium imaging data

2020 ◽  
Author(s):  
Peter Rupprecht ◽  
Stefano Carta ◽  
Adrian Hoffmann ◽  
Mayumi Echizen ◽  
Kazuo Kitamura ◽  
...  
Keyword(s):  
Calcium Imaging ◽  
Action Potentials ◽  
Sampling Rate ◽  
Ground Truth ◽  
Cell Types ◽  
Imaging Data ◽  
Calcium Signals ◽  
Ground Truth Data ◽  
Unseen Data ◽  
User Friendly

ABSTRACTCalcium imaging is a key method to record patterns of neuronal activity across populations of identified neurons. Inference of temporal patterns of action potentials (‘spikes’) from calcium signals is, however, challenging and often limited by the scarcity of ground truth data containing simultaneous measurements of action potentials and calcium signals. To overcome this problem, we compiled a large and diverse ground truth database from publicly available and newly performed recordings. This database covers various types of calcium indicators, cell types, and signal-to-noise ratios and comprises a total of >20 hours from 225 neurons. We then developed a novel algorithm for spike inference (CASCADE) that is based on supervised deep networks, takes advantage of the ground truth database, infers absolute spike rates, and outperforms existing model-based algorithms. To optimize performance for unseen imaging data, CASCADE retrains itself by resampling ground truth data to match the respective sampling rate and noise level. As a consequence, no parameters need to be adjusted by the user. To facilitate routine application of CASCADE we developed systematic performance assessments for unseen data, we openly release all resources, and we provide a user-friendly cloud-based implementation.

scholarly journals Partial Multi-Label Optimal Margin Distribution Machine

2021 ◽  
Author(s):  
Nan Cao ◽  
Teng Zhang ◽  
Hai Jin
Keyword(s):  
Ground Truth ◽  
Data Sets ◽  
Real World Data ◽  
Generalization Performance ◽  
The Core ◽  
Unseen Data ◽  
Margin Distribution ◽  
Minimum Margin ◽  
Core Problem ◽  
Noisy Labels

Partial multi-label learning deals with the circumstance in which the ground-truth labels are not directly available but hidden in a candidate label set. Due to the presence of other irrelevant labels, vanilla multi-label learning methods are prone to be misled and fail to generalize well on unseen data, thus how to enable them to get rid of the noisy labels turns to be the core problem of partial multi-label learning. In this paper, we propose the Partial Multi-Label Optimal margin Distribution Machine (PML-ODM), which distinguishs the noisy labels through explicitly optimizing the distribution of ranking margin, and exhibits better generalization performance than minimum margin based counterparts. In addition, we propose a novel feature prototype representation to further enhance the disambiguation ability, and the non-linear kernels can also be applied to promote the generalization performance for linearly inseparable data. Extensive experiments on real-world data sets validates the superiority of our proposed method.

scholarly journals A 3D Deep Neural Network for Liver Volumetry in 3T Contrast-Enhanced MRI

2020 ◽  
Author(s):  
Hinrich Winther ◽  
Christian Hundt ◽  
Kristina Imeen Ringe ◽  
Frank K. Wacker ◽  
Bertil Schmidt ◽  
...  
Keyword(s):  
Neural Network ◽  
Intraclass Correlation ◽  
Ground Truth ◽  
Hepatobiliary Phase ◽  
Dice Coefficient ◽  
Enhanced Mri ◽  
The Neural Network ◽  
Liver Volumetry ◽  
Phase Images ◽  
Mri Scans

Purpose To create a fully automated, reliable, and fast segmentation tool for Gd-EOB-DTPA-enhanced MRI scans using deep learning. Materials and Methods Datasets of Gd-EOB-DTPA-enhanced liver MR images of 100 patients were assembled. Ground truth segmentation of the hepatobiliary phase images was performed manually. Automatic image segmentation was achieved with a deep convolutional neural network. Results Our neural network achieves an intraclass correlation coefficient (ICC) of 0.987, a Sørensen–Dice coefficient of 96.7 ± 1.9 % (mean ± std), an overlap of 92 ± 3.5 %, and a Hausdorff distance of 24.9 ± 14.7 mm compared with two expert readers who corresponded to an ICC of 0.973, a Sørensen–Dice coefficient of 95.2 ± 2.8 %, and an overlap of 90.9 ± 4.9 %. A second human reader achieved a Sørensen–Dice coefficient of 95 % on a subset of the test set. Conclusion Our study introduces a fully automated liver volumetry scheme for Gd-EOB-DTPA-enhanced MR imaging. The neural network achieves competitive concordance with the ground truth regarding ICC, Sørensen–Dice, and overlap compared with manual segmentation. The neural network performs the task in just 60 seconds. Key Points:  Citation Format

Machine Learning for Brain Image Segmentation

2010 ◽  
pp. 102-126
Author(s):  
Jonathan Morra ◽  
Zhuowen Tu ◽  
Arthur Toga ◽  
Paul Thompson
Keyword(s):  
Machine Learning ◽  
Markov Random Fields ◽  
Active Contours ◽  
Brain Mri ◽  
Ground Truth ◽  
Learning Techniques ◽  
Mri Scans ◽  
Automated Algorithms ◽  
Future Work ◽  
Segmentation Problem

In this chapter, the authors review a variety of algorithms developed by different groups for automatically segmenting structures in medical images, such as brain MRI scans. Some of the simpler methods, based on active contours, deformable image registration, and anisotropic Markov random fields, have known weaknesses, which can be largely overcome by learning methods that better encode knowledge on anatomical variability. The authors show how the anatomical segmentation problem may be re-cast in a Bayesian framework. They then present several different learning techniques increasing in complexity until they derive two algorithms recently proposed by the authors. The authors show how these automated algorithms are validated empirically, by comparison with segmentations by experts, which serve as independent ground truth, and in terms of their power to detect disease effects in Alzheimer’s disease. They show how these methods can be used to investigate factors that influence disease progression in databases of thousands of images. Finally the authors indicate some promising directions for future work.

Machine Learning for Brain Image Segmentation

2012 ◽  
pp. 851-874
Author(s):  
Jonathan Morra ◽  
Zhuowen Tu ◽  
Arthur Toga ◽  
Paul Thompson
Keyword(s):  
Machine Learning ◽  
Markov Random Fields ◽  
Active Contours ◽  
Brain Mri ◽  
Ground Truth ◽  
Learning Techniques ◽  
Mri Scans ◽  
Automated Algorithms ◽  
Future Work ◽  
Segmentation Problem

In this chapter, the authors review a variety of algorithms developed by different groups for automatically segmenting structures in medical images, such as brain MRI scans. Some of the simpler methods, based on active contours, deformable image registration, and anisotropic Markov random fields, have known weaknesses, which can be largely overcome by learning methods that better encode knowledge on anatomical variability. The authors show how the anatomical segmentation problem may be re-cast in a Bayesian framework. They then present several different learning techniques increasing in complexity until they derive two algorithms recently proposed by the authors. The authors show how these automated algorithms are validated empirically, by comparison with segmentations by experts, which serve as independent ground truth, and in terms of their power to detect disease effects in Alzheimer’s disease. They show how these methods can be used to investigate factors that influence disease progression in databases of thousands of images. Finally the authors indicate some promising directions for future work.

scholarly journals  Hydrocephalus in dogs: a review

2013 ◽  
Vol 58 (No. 2) ◽  
pp. 73-80 ◽  
Author(s):  
P. Przyborowska ◽  
Z. Adamiak ◽  
M. Jaskolska ◽  
Y. Zhalniarovich
Keyword(s):  
Clinical Symptoms ◽  
Imaging Techniques ◽  
Spin Echo ◽  
Brain Area ◽  
Volume Ratio ◽  
Diagnostic Methods ◽  
Slice Thickness ◽  
Advantages And Disadvantages ◽  
Mri Scans ◽  
The Brain

Hydrocephalus is a multifactoral disorder that was rarely diagnosed in dogs until the availability of advanced imaging techniques in veterinary practice. This article reviews recent advances in the understanding of canine hydrocephalus including pathogenesis, clinical symptoms, diagnostic methods, and treatment solutions. The advantages and disadvantages of USG, RTG, CT and MRI as advanced diagnostic methods are discussed. For now Low-field Magnetic Resonance Imaging is the most useful tool in investigating hydrocephalus. The recommended sequences for MRI are T1-weighting images Spin echo, Field echo 3D with TR 380–750 ms, TE 12–25 ms, slice thickness 1–6 mm and with an interslice gap of 0–2 mm. The evaluation of cerebral ventricular system morphology in obtained MRI scans involves measuring the height, area and volume of the brain and lateral ventricles. The results are classified as normal state if the ratio of ventricular height to the brain height is above 14%, the ratio of ventricular area to the brain area amounts to above 7%, and the ventricular to brain volume ratio is above 5%. However, there are still problems relating to inter- and intrabreed comparison among examined dogs. Treatment solutions in hydrocephalus are also discussed in this review. The medical treatment of hydrocephalus aims to decrease CSF production and is based on using acetazolamide, furosemide and prednisone. Surgical management aims to place the ventriculoperitoneal shunt for CSF flow control. Postsurgical complications are also described in this review.  

Sign in / Sign up

Export Citation Format

Share Document