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