Keywords: Segmentation, Segmentation

Motivation: Despite the availability of rapid high-resolution MRF sequences that can be used to synthesize MPRAGE, acquiring MPRAGE scans remains necessary for accurate downstream segmentation.

Goal(s): The aim is to perform accurate-segmentation directly on MRF time-resolved data, eliminating the need for a lengthy MPRAGE scan, resulting in significant time savings, and providing quantitative tissue parameter maps.

Approach: We used deep learning to directly segment MRF time-resolved data and generate multi-tissue brain segmentation maps.

Results: Our findings indicate that deep learning segmentation methods trained directly on MRF data, both quantitatively and qualitatively perform better than segmentation on synthesized MPRAGE.

Impact: Applying deep learning directly on MRF data improves MRF segmentation compared to synthesizing MPRAGE and performing segmentation on it. This strengthens the validation of MRF and enhances its clinical potential by rapidly acquiring and segmenting brain images.

Keywords: Gray Matter, Machine Learning/Artificial Intelligence

Motivation: The project was driven by the need to reduce 3D T1-weighted MRI acquisition times, which are often prolonged, leading to motion artifacts and compromised image quality in structural nuroimaging analysis.

Goal(s): To evaluate whether deep learning reconstruction can shorten MRI scan times without significantly compromising image quality, facilitating efficient clinical and research neuroimaging.

Approach: We employed a deep learning technique, DL-speed, to reconstruct undersampled data from accelerated MRI scans, assessing image quality against conventional methods using a standardized rating system.

Results: Images with DL-speed maintained image quality, despite a slight quality trade-off, suggesting its viability for rapid, motion-artifact-reduced neuroimaging in various patient populations.

Impact: Our results impact clinicians and patients by enabling faster, high-quality MRIs, reducing patient discomfort and motion-related artifacts. This advance opens avenues for more efficient neuroimaging protocols, enhancing patient care and research productivity.

Keywords: Segmentation, Segmentation, Lesion segmentation; Generative model

Motivation: Deep learning shows great potential for brain lesion segmentation but poor generalization (due to limited training data) could lead to false positives.

Goal(s): Our goal was to improve the segmentation accuracy by learning target-specific posterior distributions.

Approach: We proposed a new Bayesian brain lesion segmentation method, leveraging posterior distributions learning, including both posterior normal and lesion distributions, through a subspace-assisted deep generative model.

Results: The proposed method achieved significantly improved segmentation performance across multiple public datasets with stroke, tumor, and multiple sclerosis lesions, in comparison with the state-of-the-art methods.

Impact: The proposed method significantly improved accuracy and robustness of lesions segmentation in brain MR images, which may provide a useful tool for brain lesion delineation in image processing and clinical applications.

Keywords: Segmentation, Segmentation, Brain tissue

Motivation: Motion artifacts in MRI scans present challenges by causing blurred images with tissue-like appearances, significantly complicating the tissue segmentation process.

Goal(s): Our goal is to achieve accurate brain tissue segmentation even in the presence of motion artifacts.

Approach: We propose a brain tissue segmentation method robust to motion artifacts, that generates a motion deformation map and a prediction mask for brain tissue segmentation. The motion deformation map serves as an indicator within the segmentation network, aiding in the identification of regions impacted by motion artifacts.

Results: Our method demonstrates superior performance compared to other segmentation models, especially when dealing with motion-corrupted data.

Impact: We propose a motion-robust segmentation network that incorporates prior motion knowledge via a motion estimation network. By employing a multi-task learning approach involving joint motion estimation and segmentation networks, we improve brain tissue segmentation by recovering incorrectly segmented structures.

Keywords: Segmentation, Multimodal, Multi-parametric MRI, Deep Learning

Motivation: Accurate segmentation of visual pathway (VP) in multi-parametric MRI is crucial for reliable diagnosis of visual disorders. However, existing methods face challenges due to complex multi-parametric MRI relationships and limited labeled training data.

Goal(s): The goal is to improve automatic VP delineation by developing a new framework that handles complex multi-parametric MRI relationships and incorporates unlabeled data.

Approach: Our framework incorporates a correlation-constrained feature decomposition module to better exploit multi-parametric MRI information and a consistency-based sample selection method for more effective semi-supervised learning. 

Results: Experiments on the HCP dataset show that the proposed framework achieved superior VP delineation performance compared to state-of-the-art approaches.

Impact: The results of this study could have a significant impact on scientists, clinicians, and patients by improving the understanding of the human visual system and enhancing the diagnosis accuracy of visual pathway disorders.

Keywords: Segmentation, Blood vessels

Motivation: Vessel location must be pinpointed for precise avoidance during probe insertion for liver ablation.

Goal(s): Our goal was to segment the hepatic vessel from MR images and to ensure the connectivity of the segmentation results.

Approach: Hepatic vessel MR images were obtained from the records of 105 patients. Coarse-to-fine vascular connectivity enhancement algorithm was trained and tested using a five-fold cross-validation method.

Results: Results demonstrated that our two-stage algorithm improved the connectivity of vessel segmentation results, with the dice coefficient increasing by up to 1.8% compared to the initial segmentation.

Impact: Accurate segmentation results and enhanced connectivity provide a basis for hepatic vessel location and modeling. The results can assist doctors in preoperative planning while reducing the risk of damage to normal tissue in patients.

Keywords: Segmentation, Spinal Cord, Segmentation; Deep Learning; Morphometrics; Atrophy; Variability; Reproducibility; Vendors

Motivation: Spinal cord cross-sectional area (CSA) is an important biomarker for neurodegenerative and traumatic diseases. However, CSA measurements vary across MRI contrasts and imaging protocols, limiting its use in multi-center studies.

Goal(s): The goal is to evaluate CSA variability using a novel contrast-agnostic segmentation method.

Approach: We compared this method to the Spinal Cord Toolbox's DeepSeg, analyzing CSA across different sites, and MRI vendors. Additionally, we compared the segmentations in diverse datasets and pathologies.

Results: The contrast-agnostic segmentation showed lower CSA variability, and superior performance in most cases, except for intramedullary cord compression, where the Spinal Cord Toolbox's DeepSeg was more accurate.

Impact: The contrast-agnostic method yields reliable spinal cord CSA measurements,  independent of MRI contrasts and vendors. This, combined with a soft segmentation output, can potentially detect subtle spinal cord atrophy in prospective multi-center cohorts.

Keywords: Segmentation, Joints, Wrist

Motivation: Dynamic imaging can be useful for the evaluation of wrist instability. 

Goal(s): To propose a semi-automatic approach for carpal bones segmentation on 3D dynamic wrist MRI to enable kinematic assessment.

Approach: We segmented carpal bones on a high-resolution 3D static MRI, registered it to a template created from the dynamic frames, and transferred back the segmentations onto individual 3D dynamic volumes. Bones surfaces were reconstructed and the reproducibility of  motion patterns was assessed on repeated scans.

Results: Our proposed image processing and visualization pipeline enables  semi-automatic segmentation of carpal bones and provides a framework for qualitative and quantitative analysis of wrist  kinematics.

Impact: This work demonstrates semi-automatic segmentation of real-time dynamic MRI of the wrist to extract carpal bones motion. It could be used for quantitative kinematic analysis to detect and characterize wrist abnormalities.

Keywords: Segmentation, Segmentation, Susceptibility Imaging, Basal Ganglia, Segmentation Network, Magnitude Information Complete, Active Gradient Guidance

Motivation: Accurate segmentation of basal ganglia is a crucial prerequisite for subsequent clinical practice and research. The boundaries of BG remain challenging to segment especially when dealing with data affected by severe artifacts.

Goal(s): This work aims to propose an automatic BG segmentation method with radiologist comparable accuracy and high inference speed.

Approach: An active gradient guidance-based susceptibility and magnitude information complete network(AGNet). With newly designed modules, AGNet can efficiently capture the inter-slice information and exploit it as attention guidance to facilitate the segmentation process.

Results: AGNet has superior segment accuracy over existing methods with ADSC=0.874 and AHD=2.010, especially near boundaries of target VOI.

Impact: The proposed model achieves more accurate segmentation at the boundary contour. Automatic and precise segmentation of basal ganglia is a prerequisite for the quantification of tissue magnetic susceptibility analysis and can serve as a fundamental tool for neurodegenerative disease research.

Keywords: Segmentation, Segmentation

Motivation: Whole-brain MRI parcellation serves as a feature extraction technique, allowing for the condensation of over a million pixels of information into a few hundred neuroanatomically defined elements.

Goal(s): The multi-atlas label-fusion (MALF) method is known for accurate parcellation but typically necessitates several hours to process a single image. Our goal was to develop a faster parcellation tool with an accuracy comparable to that of MALF.

Approach: We introduce open-source multiple anatomical parcellation T1 (OpenMAP-T1), based on deep learning and multi-processing.

Results: The OpenMAP achieves an equivalent parcellation performance to MALF and is 40 times faster.

Impact: OpenMAP significantly accelerates processing speed, allowing for large-scale data analysis using volumetric information derived from detailed parcellation of the whole brain, including both gray and white matter regions.