1,465 research outputs found
The Brain Tumor Sequence Registration Challenge: Establishing Correspondence between Pre-Operative and Follow-up MRI scans of diffuse glioma patients
Registration of longitudinal brain Magnetic Resonance Imaging (MRI) scans containing pathologies is challenging due to tissue appearance changes, and still an unsolved problem. This paper describes the first Brain Tumor Sequence Registration (BraTS-Reg) challenge, focusing on estimating correspondences between pre-operative and follow-up scans of the same patient diagnosed with a brain diffuse glioma. The BraTS-Reg challenge intends to establish a public benchmark environment for deformable registration algorithms. The associated dataset comprises de-identified multi-institutional multi-parametric MRI (mpMRI) data, curated for each scan's size and resolution, according to a common anatomical template. Clinical experts have generated extensive annotations of landmarks points within the scans, descriptive of distinct anatomical locations across the temporal domain. The training data along with these ground truth annotations will be released to participants to design and develop their registration algorithms, whereas the annotations for the validation and the testing data will be withheld by the organizers and used to evaluate the containerized algorithms of the participants. Each submitted algorithm will be quantitatively evaluated using several metrics, such as the Median Absolute Error (MAE), Robustness, and the Jacobian determinant
Self-supervised iRegNet for the Registration of Longitudinal Brain MRI of Diffuse Glioma Patients
Reliable and accurate registration of patient-specific brain magnetic
resonance imaging (MRI) scans containing pathologies is challenging due to
tissue appearance changes. This paper describes our contribution to the
Registration of the longitudinal brain MRI task of the Brain Tumor Sequence
Registration Challenge 2022 (BraTS-Reg 2022). We developed an enhanced
unsupervised learning-based method that extends the iRegNet. In particular,
incorporating an unsupervised learning-based paradigm as well as several minor
modifications to the network pipeline, allows the enhanced iRegNet method to
achieve respectable results. Experimental findings show that the enhanced
self-supervised model is able to improve the initial mean median registration
absolute error (MAE) from 8.20 (7.62) mm to the lowest value of 3.51 (3.50) for
the training set while achieving an MAE of 2.93 (1.63) mm for the validation
set. Additional qualitative validation of this study was conducted through
overlaying pre-post MRI pairs before and after the de-formable registration.
The proposed method scored 5th place during the testing phase of the MICCAI
BraTS-Reg 2022 challenge. The docker image to reproduce our BraTS-Reg
submission results will be publicly available.Comment: Accepted in the MICCAI BraTS-Reg 2022 Challenge (as part of the
BrainLes workshop proceedings distributed by Springer LNCS
The University of Pennsylvania Glioblastoma (UPenn-GBM) cohort: Advanced MRI, clinical, genomics, & radiomics
Glioblastoma is the most common aggressive adult brain tumor. Numerous studies have reported results from either private institutional data or publicly available datasets. However, current public datasets are limited in terms of: a) number of subjects, b) lack of consistent acquisition protocol, c) data quality, or d) accompanying clinical, demographic, and molecular information. Toward alleviating these limitations, we contribute the University of Pennsylvania Glioblastoma Imaging, Genomics, and Radiomics (UPenn-GBM) dataset, which describes the currently largest publicly available comprehensive collection of 630 patients diagnosed with de novo glioblastoma. The UPenn-GBM dataset includes (a) advanced multi-parametric magnetic resonance imaging scans acquired during routine clinical practice, at the University of Pennsylvania Health System, (b) accompanying clinical, demographic, and molecular information, (d) perfusion and diffusion derivative volumes, (e) computationally-derived and manually-revised expert annotations of tumor sub-regions, as well as (f) quantitative imaging (also known as radiomic) features corresponding to each of these regions. This collection describes our contribution towards repeatable, reproducible, and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments
Spatial distribution of malignant transformation in patients with low-grade glioma
Background
Malignant transformation represents the natural evolution of diffuse low-grade gliomas (LGG). This is a catastrophic event, causing neurocognitive symptoms, intensified treatment and premature death. However, little is known concerning the spatial distribution of malignant transformation in patients with LGG.
Materials and methods
Patients histopathological diagnosed with LGG and subsequent radiological malignant transformation were identified from two different institutions. We evaluated the spatial distribution of malignant transformation with (1) visual inspection and (2) segmentations of longitudinal tumor volumes. In (1) a radiological transformation site < 2 cm from the tumor on preceding MRI was defined local transformation. In (2) overlap with pretreatment volume after importation into a common space was defined as local transformation. With a centroid model we explored if there were particular patterns of transformations within relevant subgroups.
Results
We included 43 patients in the clinical evaluation, and 36 patients had MRIs scans available for longitudinal segmentations. Prior to malignant transformation, residual radiological tumor volumes were > 10 ml in 93% of patients. The transformation site was considered local in 91% of patients by clinical assessment. Patients treated with radiotherapy prior to transformation had somewhat lower rate of local transformations (83%). Based upon the segmentations, the transformation was local in 92%. We did not observe any particular pattern of transformations in examined molecular subgroups.
Conclusion
Malignant transformation occurs locally and within the T2w hyperintensities in most patients. Although LGG is an infiltrating disease, this data conceptually strengthens the role of loco-regional treatments in patients with LGG.publishedVersio
Primitive Simultaneous Optimization of Similarity Metrics for Image Registration
Even though simultaneous optimization of similarity metrics represents a
standard procedure in the field of semantic segmentation, surprisingly, this
does not hold true for image registration. To close this unexpected gap in the
literature, we investigate in a complex multi-modal 3D setting whether
simultaneous optimization of registration metrics, here implemented by means of
primitive summation, can benefit image registration. We evaluate two
challenging datasets containing collections of pre- to post-operative and pre-
to intra-operative Magnetic Resonance Imaging (MRI) of glioma. Employing the
proposed optimization we demonstrate improved registration accuracy in terms of
Target Registration Error (TRE) on expert neuroradiologists' landmark
annotations
Full Issue: Volume 13, Issue 1 - Winter 2018
Full Issue: Volume 13, Issue 1 - Winter 201
Focal Spot, Spring 2004
https://digitalcommons.wustl.edu/focal_spot_archives/1096/thumbnail.jp
- …