Magnetic Resonance Imaging (MRI) plays a pivotal role in diagnosing and predicting the course of Multiple Sclerosis (MS). A distinctive biomarker, Paramagnetic Rim Lesions (PRL), offers promise but poses challenges in manual assessment. To address this, we introduce a direct PRL segmentation approach and extensively evaluate various methods, with a focus on preprocessing and input modalities. Our study emphasizes instance segmentation metrics tailored for sparse lesions. Single-modal inputs show limitations, except for FLAIR and Magnitude, exhibiting potential in PRL detection. Integrating Phase and/or MPRAGE with FLAIR enhances the detection capacity. Notably, applying white matter masks yields mixed results, while lesion masks improve overall performance. Despite the complexities of PRL segmentation, our optimal model, FLAIR+Phase, attains a F1 score of 0.443, a Dice score coefficient per True Positive of 0.68 and a deceiving Dice score of 0.191 on the test set. This highlights the intricate nature of the PRL segmentation task. Our work pioneers an automated approach to PRL analysis, offering valuable insights and paving the way for future advancements in this critical field.
In fetal brain MRI, most of the high-resolution reconstruction algorithms rely on brain segmentation as a preprocessing step. Manual brain segmentation is however highly time-consuming and therefore not a realistic solution. In this work, we assess on a large dataset the performance of Multiple Atlas Fusion (MAF) strategies to automatically address this problem. Firstly, we show that MAF significantly increase the accuracy of brain segmentation as regards single-atlas strategy. Secondly, we show that MAF compares favorably with the most recent approach (Dice above 0.90). Finally, we show that MAF could in turn provide an enhancement in terms of reconstruction quality.
In this paper we present a new method to track bone movements in stereoscopic X-ray image series of the knee joint. The method is based on two different X-ray image sets: a rotational series of acquisitions of the still subject knee that allows the tomographic reconstruction of the three-dimensional volume (model), and a stereoscopic image series of orthogonal projections as the subject performs movements. Tracking the movements of bones throughout the stereoscopic image series means to determine, for each frame, the best pose of every moving element (bone) previously identified in the 3D reconstructed model. The quality of a pose is reflected in the similarity between its theoretical projections and the actual radiographs.
We use direct Fourier reconstruction to approximate the three-dimensional volume of the knee joint. Then, to avoid the expensive computation of digitally rendered radiographs (DRR) for pose recovery, we develop a corollary to the 3-dimensional central-slice theorem and reformulate the tracking problem in the Fourier domain. Under the hypothesis of parallel X-ray beams, the heavy 2D-to-3D registration of projections in the signal domain is replaced by efficient slice-to-volume registration in the Fourier domain. Focusing on rotational movements, the translation-relevant phase information can be discarded and we only consider scalar Fourier amplitudes. The core of our motion tracking algorithm can be implemented as a classical frame-wise slice-to-volume registration task. Results on both synthetic and real images confirm the validity of our approach.
Conference Committee Involvement (12)
Image Processing
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Image Processing
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Image Processing Posters
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Image Processing
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