A commercial deep learning (DL)-based automated segmentation tool (AST) for computed tomography (CT) is evaluated for accuracy and efficiency gain within prostate cancer patients. Thirty patients ...from six clinics were reviewed with manual- (MC), automated- (AC) and automated and edited (AEC) contouring methods. In the AEC group, created contours (prostate, seminal vesicles, bladder, rectum, femoral heads and penile bulb) were edited, whereas the MC group included empty datasets for MC. In one clinic, lymph node CTV delineations were evaluated for interobserver variability. Compared to MC, the mean time saved using the AST was 12 min for the whole data set (46%) and 12 min for the lymph node CTV (60%), respectively. The delineation consistency between MC and AEC groups according to the Dice similarity coefficient (DSC) improved from 0.78 to 0.94 for the whole data set and from 0.76 to 0.91 for the lymph nodes. The mean DSCs between MC and AC for all six clinics were 0.82 for prostate, 0.72 for seminal vesicles, 0.93 for bladder, 0.84 for rectum, 0.69 for femoral heads and 0.51 for penile bulb. This study proves that using a general DL-based AST for CT images saves time and improves consistency.
The loss of nuclear factor E2-related factor 2 (Nrf2) has been shown to protect against atherogenesis in apoE-deficient mice. The mechanism by which Nrf2 deficiency affords atheroprotection in this ...model is currently unknown, but combined systemic and local vascular effects on lesion macrophages have been proposed. We investigated the effect of bone marrow-specific loss of Nrf2 on early atherogenesis in low-density lipoprotein (LDL) receptor-deficient (LDLR(-/-)) mice, and assessed the effect of Nrf2 on cellular accumulation of modified LDLs and the expression of inflammatory markers in macrophages.
The effect of bone marrow-specific loss of Nrf2 on atherogenesis was studied using bone marrow transplantation of wild-type (WT) or Nrf2(-/-) bone marrow to LDLR(-/-) mice. Mice transplanted with Nrf2(-/-) bone marrow and fed a high-fat diet for 6 weeks exhibited significantly larger atherosclerotic lesions than WT bone marrow transplanted mice. Moreover, in thioglycollate-elicited Nrf2(-/-) macrophages, the uptake of acetylated and malondialdehyde-modified LDLs was increased in comparison with WT controls, with the concomitant increase in the expression of scavenger receptor A and toll-like receptor 4. In addition, the expression of pro-inflammatory monocyte chemoattractant protein-1 and interleukin-6 were increased in Nrf2(-/-) vs. WT macrophages.
Nrf2 deficiency specific to bone marrow-derived cells aggravates atherosclerosis in LDLR(-/-) mice. Furthermore, the loss of Nrf2 in macrophages enhances foam cell formation and promotes the pro-inflammatory phenotype.
We present an algorithm that simultaneously calibrates two color cameras, a depth camera, and the relative pose between them. The method is designed to have three key features: accurate, practical, ...and applicable to a wide range of sensors. The method requires only a planar surface to be imaged from various poses. The calibration does not use depth discontinuities in the depth image, which makes it flexible and robust to noise. We apply this calibration to a Kinect device and present a new depth distortion model for the depth sensor. We perform experiments that show an improved accuracy with respect to the manufacturer's calibration.
Many computer vision applications require robust and efficient estimation of camera geometry from a minimal number of input data measurements. Minimal problems are usually formulated as complex ...systems of sparse polynomial equations. The systems usually are overdetermined and consist of polynomials with algebraically constrained coefficients. Most state-of-the-art efficient polynomial solvers are based on the action matrix method that has been automated and highly optimized in recent years. On the other hand, the alternative theory of sparse resultants based on the Newton polytopes has not been used so often for generating efficient solvers, primarily because the polytopes do not respect the constraints amongst the coefficients. In an attempt to tackle this challenge, here we propose a simple iterative scheme to test various subsets of the Newton polytopes and search for the most efficient solver. Moreover, we propose to use an extra polynomial with a special form to further improve the solver efficiency via Schur complement computation. We show that for some camera geometry problems our resultant-based method leads to smaller and more stable solvers than the state-of-the-art Gröbner basis-based solvers, while being significantly smaller than the state-of-the-art resultant-based methods. The proposed method can be fully automated and incorporated into existing tools for the automatic generation of efficient polynomial solvers. It provides a competitive alternative to popular Gröbner basis-based methods for minimal problems in computer vision. Additionally, we study the conditions under which the minimal solvers generated by the state-of-the-art action matrix-based methods and the proposed extra polynomial resultant-based method, are equivalent. Specifically, we consider a step-by-step comparison between the approaches based on the action matrix and the sparse resultant, followed by a set of substitutions, which would lead to equivalent minimal solvers.
Despite the impressive performance of recent unbiased Scene Graph Generation (SGG) methods, the current debiasing literature mainly focuses on the long-tailed distribution problem, whereas it ...overlooks another source of bias, i.e., semantic confusion, which makes the SGG model prone to yield false predictions for similar relationships. In this paper, we explore a debiasing procedure for the SGG task leveraging causal inference. Our central insight is that the Sparse Mechanism Shift (SMS) in causality allows independent intervention on multiple biases, thereby potentially preserving head category performance while pursuing the prediction of high-informative tail relationships. However, the noisy datasets lead to unobserved confounders for the SGG task, and thus the constructed causal models are always causal-insufficient to benefit from SMS. To remedy this, we propose Two-stage Causal Modeling (TsCM) for the SGG task, which takes the long-tailed distribution and semantic confusion as confounders to the Structural Causal Model (SCM) and then decouples the causal intervention into two stages. The first stage is causal representation learning, where we use a novel Population Loss (P-Loss) to intervene in the semantic confusion confounder. The second stage introduces the Adaptive Logit Adjustment (AL-Adjustment) to eliminate the long-tailed distribution confounder to complete causal calibration learning. These two stages are model agnostic and thus can be used in any SGG model that seeks unbiased predictions. Comprehensive experiments conducted on the popular SGG backbones and benchmarks show that our TsCM can achieve state-of-the-art performance in terms of mean recall rate. Furthermore, TsCM can maintain a higher recall rate than other debiasing methods, which indicates that our method can achieve a better tradeoff between head and tail relationships.
Aims Macrophage scavenger receptor A (SR-A) and class B scavenger receptor CD36 (CD36) contribute to foam cell formation and atherogenesis via uptake of modified lipoproteins. So far, the role of ...these scavenger receptors has been studied mainly using knockout models totally lacking these receptors. We studied the role of SR-A and CD36 in foam cell formation and atherogenesis by RNA interference (RNAi)-mediated silencing, which is a clinically feasible method to down-regulate the expression of these receptors. Methods and results We constructed lentivirus vectors encoding short hairpin RNAs (shRNAs) against mouse SR-A and CD36. Decreased SR-A but not CD36 expression led to reduced foam cell formation caused by acetylated low-density lipoprotein (LDL) in mouse macrophages, whereas the uptake of oxidized LDL was not altered. More importantly, silencing of SR-A upregulates CD36 and vice versa. In LDL receptor-deficient apolipoprotein B100 (LDLR−/−ApoB100/100) mice kept on a western diet, silencing of either SR-A or CD36 in bone marrow cells led to a marked decrease (37.4 and 34.2%, respectively) in cross-sectional lesion area, whereas simultaneous silencing of both receptors was not effective. Conclusion Our results suggest that silencing of either SR-A or CD36 alone reduces atherogenesis in mice. However, due to reciprocal upregulation, silencing of both SR-A and CD36 is not effective.
We present a method to locally reconstruct dense video depth maps of a non-rigidly deformable object directly from a video sequence acquired by a static orthographic camera. The estimation of depth ...is performed locally on spatiotemporal patches of the video, and then, the full depth video is recovered by combining them together. Since the geometric complexity of a local spatiotemporal patch of a deforming non-rigid object is often simple enough to be faithfully represented with a parametric model, we artificially generate a database of small deforming rectangular meshes rendered with different material properties and light conditions, along with their corresponding depth videos, and use such data to train a convolutional neural network. Since the database images are rendered with an orthographic camera model, linear deformations along the optical axis cannot be recovered from the training images. These are known in the literature as generalized bas-relief (GBR) transformations. We address this ambiguity problem by employing the invariant-theoretic
normalization procedure
in order to obtain complete invariants with respect to this group of transformations, and use them in the loss function of a neural network. We tested our method on both synthetic and Kinect data and experimentally observed that the reconstruction error is significantly lower than the one obtained using conventional non-rigid structure from motion approaches and state-of-the-art video depth estimation techniques.
Kidney development depends crucially on proper ureteric bud branching giving rise to the entire collecting duct system. The transcription factor HNF1B is required for the early steps of ureteric bud ...branching, yet the molecular and cellular events regulated by HNF1B are poorly understood. We report that specific removal of
from the ureteric bud leads to defective cell-cell contacts and apicobasal polarity during the early branching events. High-resolution
imaging combined with a membranous fluorescent reporter strategy show decreased mutant cell rearrangements during mitosis-associated cell dispersal and severe epithelial disorganization. Molecular analysis reveals downregulation of Gdnf-Ret pathway components and suggests that HNF1B acts both upstream and downstream of Ret signaling by directly regulating
and
Subsequently,
deletion leads to massively mispatterned ureteric tree network, defective collecting duct differentiation and disrupted tissue architecture, which leads to cystogenesis. Consistently, mRNA-seq analysis shows that the most impacted genes encode intrinsic cell-membrane components with transporter activity. Our study uncovers a fundamental and recurring role of HNF1B in epithelial organization during early ureteric bud branching and in further patterning and differentiation of the collecting duct system in mouse.
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