9 research outputs found
QUALITY ASSURANCE USING OUTLIER DETECTIONS FOR CEREBELLAR LOBULE SEGMENTATION
The cerebellum plays an important role in motor control and cognitive activities. In recent years, several methods have been proposed for automatic cerebellum parcellation. Usually, the segmentation accuracy is evaluated by comparing with manual delineations directly. However, such comparison is impractical in real segmentation scenarios where no manual delineations are available. What is worse, when a segmentation software fails to give an accurate result, the failed segmentation will inevitably bias further studies. Therefore, there is need for an automatic approach that can detect segmentation failures and guarantee the quality of segmentation results.
The thesis has two main focuses: evaluating and validating a new approach for cerebellar lobule segmentation and designing an automatic approach for the Quality Assurance (QA) of a cerebellar lobule segmentation pipeline. In the first part of the thesis, we formulate the task of QA and introduce several metrics for evaluating the performance of segmentation software in medical image analysis. We then evaluate a newly proposed cerebellar lobule segmentation software using the introduced metrics. Statistical results show that the segmentation software can give reliable cerebellar lobule segmentation results in a reasonable amount of time while sometimes the software has segmentation failures.
The second part of the thesis focuses on automatic QA using outlier detection methods. We introduce a new approach that can automatically detect segmentation failures in a set of segmentation results. The proposed QA approach analyzes all the important processing steps of a segmentation software. In addition, the proposed method provides a general framework of QA that can be modified and applied to other image processing software. Experiments were done on two datasets including healthy controls and subjects with disease. Quantitative results show that the proposed QA method achieves both high sensitivity and high specificity in outlier detection. Qualitative results show that the method can find abnormalities in a set of segmentation results, which should give researchers clues about how their segmentation algorithms perform on a new dataset without ground truth
Coordinate Translator for Learning Deformable Medical Image Registration
The majority of deep learning (DL) based deformable image registration
methods use convolutional neural networks (CNNs) to estimate displacement
fields from pairs of moving and fixed images. This, however, requires the
convolutional kernels in the CNN to not only extract intensity features from
the inputs but also understand image coordinate systems. We argue that the
latter task is challenging for traditional CNNs, limiting their performance in
registration tasks. To tackle this problem, we first introduce Coordinate
Translator, a differentiable module that identifies matched features between
the fixed and moving image and outputs their coordinate correspondences without
the need for training. It unloads the burden of understanding image coordinate
systems for CNNs, allowing them to focus on feature extraction. We then propose
a novel deformable registration network, im2grid, that uses multiple Coordinate
Translator's with the hierarchical features extracted from a CNN encoder and
outputs a deformation field in a coarse-to-fine fashion. We compared im2grid
with the state-of-the-art DL and non-DL methods for unsupervised 3D magnetic
resonance image registration. Our experiments show that im2grid outperforms
these methods both qualitatively and quantitatively
Optimal operating MR contrast for brain ventricle parcellation
Development of MR harmonization has enabled different contrast MRIs to be
synthesized while preserving the underlying anatomy. In this paper, we use
image harmonization to explore the impact of different T1-w MR contrasts on a
state-of-the-art ventricle parcellation algorithm VParNet. We identify an
optimal operating contrast (OOC) for ventricle parcellation; by showing that
the performance of a pretrained VParNet can be boosted by adjusting contrast to
the OOC
Harmonization-enriched domain adaptation with light fine-tuning for multiple sclerosis lesion segmentation
Deep learning algorithms utilizing magnetic resonance (MR) images have
demonstrated cutting-edge proficiency in autonomously segmenting multiple
sclerosis (MS) lesions. Despite their achievements, these algorithms may
struggle to extend their performance across various sites or scanners, leading
to domain generalization errors. While few-shot or one-shot domain adaptation
emerges as a potential solution to mitigate generalization errors, its efficacy
might be hindered by the scarcity of labeled data in the target domain. This
paper seeks to tackle this challenge by integrating one-shot adaptation data
with harmonized training data that incorporates labels. Our approach involves
synthesizing new training data with a contrast akin to that of the test domain,
a process we refer to as "contrast harmonization" in MRI. Our experiments
illustrate that the amalgamation of one-shot adaptation data with harmonized
training data surpasses the performance of utilizing either data source in
isolation. Notably, domain adaptation using exclusively harmonized training
data achieved comparable or even superior performance compared to one-shot
adaptation. Moreover, all adaptations required only minimal fine-tuning,
ranging from 2 to 5 epochs for convergence
Rapid Brain Meninges Surface Reconstruction with Layer Topology Guarantee
The meninges, located between the skull and brain, are composed of three
membrane layers: the pia, the arachnoid, and the dura. Reconstruction of these
layers can aid in studying volume differences between patients with
neurodegenerative diseases and normal aging subjects. In this work, we use
convolutional neural networks (CNNs) to reconstruct surfaces representing
meningeal layer boundaries from magnetic resonance (MR) images. We first use
the CNNs to predict the signed distance functions (SDFs) representing these
surfaces while preserving their anatomical ordering. The marching cubes
algorithm is then used to generate continuous surface representations; both the
subarachnoid space (SAS) and the intracranial volume (ICV) are computed from
these surfaces. The proposed method is compared to a state-of-the-art
deformable model-based reconstruction method, and we show that our method can
reconstruct smoother and more accurate surfaces using less computation time.
Finally, we conduct experiments with volumetric analysis on both subjects with
multiple sclerosis and healthy controls. For healthy and MS subjects, ICVs and
SAS volumes are found to be significantly correlated to sex (p<0.01) and age
(p<0.03) changes, respectively.Comment: ISBI 2023 Ora
HACA3: A Unified Approach for Multi-site MR Image Harmonization
The lack of standardization is a prominent issue in magnetic resonance (MR)
imaging. This often causes undesired contrast variations due to differences in
hardware and acquisition parameters. In recent years, MR harmonization using
image synthesis with disentanglement has been proposed to compensate for the
undesired contrast variations. Despite the success of existing methods, we
argue that three major improvements can be made. First, most existing methods
are built upon the assumption that multi-contrast MR images of the same subject
share the same anatomy. This assumption is questionable since different MR
contrasts are specialized to highlight different anatomical features. Second,
these methods often require a fixed set of MR contrasts for training (e.g.,
both Tw-weighted and T2-weighted images must be available), which limits their
applicability. Third, existing methods generally are sensitive to imaging
artifacts. In this paper, we present a novel approach, Harmonization with
Attention-based Contrast, Anatomy, and Artifact Awareness (HACA3), to address
these three issues. We first propose an anatomy fusion module that enables
HACA3 to respect the anatomical differences between MR contrasts. HACA3 is also
robust to imaging artifacts and can be trained and applied to any set of MR
contrasts. Experiments show that HACA3 achieves state-of-the-art performance
under multiple image quality metrics. We also demonstrate the applicability of
HACA3 on downstream tasks with diverse MR datasets acquired from 21 sites with
different field strengths, scanner platforms, and acquisition protocols
QUALITY ASSURANCE USING OUTLIER DETECTIONS FOR CEREBELLAR LOBULE SEGMENTATION
The cerebellum plays an important role in motor control and cognitive activities. In recent years, several methods have been proposed for automatic cerebellum parcellation. Usually, the segmentation accuracy is evaluated by comparing with manual delineations directly. However, such comparison is impractical in real segmentation scenarios where no manual delineations are available. What is worse, when a segmentation software fails to give an accurate result, the failed segmentation will inevitably bias further studies. Therefore, there is need for an automatic approach that can detect segmentation failures and guarantee the quality of segmentation results.
The thesis has two main focuses: evaluating and validating a new approach for cerebellar lobule segmentation and designing an automatic approach for the Quality Assurance (QA) of a cerebellar lobule segmentation pipeline. In the first part of the thesis, we formulate the task of QA and introduce several metrics for evaluating the performance of segmentation software in medical image analysis. We then evaluate a newly proposed cerebellar lobule segmentation software using the introduced metrics. Statistical results show that the segmentation software can give reliable cerebellar lobule segmentation results in a reasonable amount of time while sometimes the software has segmentation failures.
The second part of the thesis focuses on automatic QA using outlier detection methods. We introduce a new approach that can automatically detect segmentation failures in a set of segmentation results. The proposed QA approach analyzes all the important processing steps of a segmentation software. In addition, the proposed method provides a general framework of QA that can be modified and applied to other image processing software. Experiments were done on two datasets including healthy controls and subjects with disease. Quantitative results show that the proposed QA method achieves both high sensitivity and high specificity in outlier detection. Qualitative results show that the method can find abnormalities in a set of segmentation results, which should give researchers clues about how their segmentation algorithms perform on a new dataset without ground truth
Disentangling A Single MR Modality
Disentangling anatomical and contrast information from medical images has
gained attention recently, demonstrating benefits for various image analysis
tasks. Current methods learn disentangled representations using either paired
multi-modal images with the same underlying anatomy or auxiliary labels (e.g.,
manual delineations) to provide inductive bias for disentanglement. However,
these requirements could significantly increase the time and cost in data
collection and limit the applicability of these methods when such data are not
available. Moreover, these methods generally do not guarantee disentanglement.
In this paper, we present a novel framework that learns theoretically and
practically superior disentanglement from single modality magnetic resonance
images. Moreover, we propose a new information-based metric to quantitatively
evaluate disentanglement. Comparisons over existing disentangling methods
demonstrate that the proposed method achieves superior performance in both
disentanglement and cross-domain image-to-image translation tasks