Better Generalization of White Matter Tract Segmentation to Arbitrary Datasets with Scaled Residual Bootstrap

White matter (WM) tract segmentation is a crucial step for brain connectivity studies. It is performed on diffusion magnetic resonance imaging (dMRI), and deep neural networks (DNNs) have achieved promising segmentation accuracy. Existing DNN-based methods use an annotated dataset for model training. However, the performance of the trained model on a different test dataset may not be optimal due to distribution shift, and it is desirable to design WM tract segmentation approaches that allow better generalization of the segmentation model to arbitrary test datasets. In this work, we propose a WM tract segmentation approach that improves the generalization with scaled residual bootstrap. The difference between dMRI scans in training and test datasets is most noticeably caused by the different numbers of diffusion gradients and noise levels. Since both of them lead to different signal-to-noise ratios (SNRs) between the training and test data, we propose to augment the training scans by adjusting the noise magnitude and develop an adapted residual bootstrap strategy for the augmentation. To validate the proposed approach, two dMRI datasets were used, and the experimental results show that our method consistently improved the generalization of WM tract segmentation under various settings

Positive-unlabeled learning for binary and multi-class cell detection in histopathology images with incomplete annotations

Cell detection in histopathology images is of great interest to clinical practice and research, and convolutional neural networks (CNNs) have achieved remarkable cell detection results. Typically, to train CNN-based cell detection models, every positive instance in the training images needs to be annotated, and instances that are not labeled as positive are considered negative samples. However, manual cell annotation is complicated due to the large number and diversity of cells, and it can be difficult to ensure the annotation of every positive instance. In many cases, only incomplete annotations are available, where some of the positive instances are annotated and the others are not, and the classification loss term for negative samples in typical network training becomes incorrect. In this work, to address this problem of incomplete annotations, we propose to reformulate the training of the detection network as a positive-unlabeled learning problem. Since the instances in unannotated regions can be either positive or negative, they have unknown labels. Using the samples with unknown labels and the positively labeled samples, we first derive an approximation of the classification loss term corresponding to negative samples for binary cell detection, and based on this approximation we further extend the proposed framework to multi-class cell detection. For evaluation, experiments were performed on four publicly available datasets. The experimental results show that our method improves the performance of cell detection in histopathology images given incomplete annotations for network training.Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2022:027. arXiv admin note: text overlap with arXiv:2106.1591

Estimation of Fiber Orientations Using Neighborhood Information

Data from diffusion magnetic resonance imaging (dMRI) can be used to reconstruct fiber tracts, for example, in muscle and white matter. Estimation of fiber orientations (FOs) is a crucial step in the reconstruction process and these estimates can be corrupted by noise. In this paper, a new method called Fiber Orientation Reconstruction using Neighborhood Information (FORNI) is described and shown to reduce the effects of noise and improve FO estimation performance by incorporating spatial consistency. FORNI uses a fixed tensor basis to model the diffusion weighted signals, which has the advantage of providing an explicit relationship between the basis vectors and the FOs. FO spatial coherence is encouraged using weighted l1-norm regularization terms, which contain the interaction of directional information between neighbor voxels. Data fidelity is encouraged using a squared error between the observed and reconstructed diffusion weighted signals. After appropriate weighting of these competing objectives, the resulting objective function is minimized using a block coordinate descent algorithm, and a straightforward parallelization strategy is used to speed up processing. Experiments were performed on a digital crossing phantom, ex vivo tongue dMRI data, and in vivo brain dMRI data for both qualitative and quantitative evaluation. The results demonstrate that FORNI improves the quality of FO estimation over other state of the art algorithms.Comment: Journal paper accepted in Medical Image Analysis. 35 pages and 16 figure

Fiber Tracking and Fiber Tract Segmentation Using Diffusion Tensor Imaging

Diffusion tensor imaging (DTI) has become a popular tool for noninvasively investigating fiber tract structures. Fiber tracking and tract segmentation are two major tasks in DTI studies. However, fiber crossing is a well known issue in DTI because DTI cannot model crossing fiber orientations (FOs). Therefore, fiber tracking and tract segmentation methods that are able to address crossing fibers are needed. In this thesis, three contributions are made to the development of such fiber tracking and tract segmentation algorithms. First, a fiber tracking method guided by volumetric tract segmentation is presented. Tract segmentation contains anatomical information which can reduce the errors caused by crossing fibers and noise. The FO estimation problem is formulated in a Bayesian framework and the resulting objective function is solved with calculus of variations. The proposed method is able to reduce false positive fibers and generate fibers that correspond to known anatomy. It is also applied to a brain connectome study to show its potential scientific application. Second, we present an algorithm for resolving crossing fibers in situations where limited diffusion gradient directions are achievable. In particular, the algorithm is focused on interdigitated tongue muscles. It incorporates prior knowledge on likely FOs to account for the insufficient information due to limited diffusion gradient directions. Using maximum a posteriori estimation, FOs can be estimated by solving a weighted $\ell_{1}$-norm regularized least squares minimization. The method is shown to reduce the effect of noise and resolve crossing fibers with limited DTI. The distributions of the computed FOs in both the controls and the patients were also compared, suggesting a potential clinical use for this methodology. Third, a white matter tract segmentation method is proposed. The method focuses on the cerebellar peduncles, which are major white matter tracts in the cerebellum. The method uses volumetric segmentation concepts based on extracted DTI features. The crossing and noncrossing portions of the peduncles are modeled as separate objects. They are initially classified using a random forest classifier together with the DTI features, and then refined by a multi-object geometric deformable model. The method is shown to achieve better segmentation results than two atlas-based methods. In the study on spinocerebellar ataxia type 6 (SCA6), the proposed method is shown to reveal anatomical changes in the patients, which demonstrates the benefit of the method for scientific purposes

Highly efficient forward osmosis based on porous membranes : applications and implications

For the first time, forward osmosis (FO) was performed using a porous membrane with an ultrafiltration (UF)-like rejection layer and its feasibility for high performance FO filtration was demonstrated. Compared to traditional FO membranes with dense rejection layers, the UF-like FO membrane was 2 orders of magnitude more permeable. This gave rise to respectable FO water flux even at ultralow osmotic driving force, for example, 7.6 L/m2.h at an osmotic pressure of merely 0.11 bar (achieved by using a 0.1% poly(sodium 4-styrene-sulfonate) draw solution). The membrane was applied to oil/water separation, and a highly stable FO water flux was achieved. The adoption of porous FO membranes opens a door to many new opportunities, with potential applications ranging from wastewater treatment, valuable product recovery, and biomedical applications. The potential applications and implications of porous FO membranes are addressed in this paper.MOE (Min. of Education, S’pore)Accepted versio