Genomic analysis reveals deep population divergence in the water snake Trimerodytes percarinatus (Serpentes, Natricidae).

Although several phylogeographic studies of Asian snakes have been conducted, most have focused on pitvipers, with non-venomous snakes, such as colubrids or natricids, remaining poorly studied. The Chinese keelback water snake (Trimerodytes percarinatus Boulenger) is a widespread, semiaquatic, non-venomous species occurring in China and southeastern Asia. Based on mitochondrial DNA (mtDNA) and single nucleotide polymorphism (SNP) data, we explored the population genetic structure, genetic diversity, and evolutionary history of this species. MtDNA-based phylogenetic analysis showed that T. percarinatus was composed of five highly supported and geographically structured lineages. SNP-based phylogenetic analysis, principal component analysis, and population structure analysis consistently revealed four distinct, geographically non-overlapping lineages, which was different from the mtDNA-based analysis in topology. Estimation of divergence dates and ancestral area of origin suggest that T. percarinatus originated ~12.68 million years ago (95% highest posterior density: 10.36-15.96 Mya) in a region covering southwestern China and Vietnam. Intraspecific divergence may have been triggered by the Qinghai-Xizang Plateau uplift. Population demographics and ecological niche modeling indicated that the effective population size fluctuated during 0.5 Mya and 0.002 Mya. Based on the data collected here, we also comment on the intraspecific taxonomy of T. percarinatus and question the validity of the subspecies T. p. suriki.

It's a trap?! Escape from an ancient, ancestral sex chromosome system and implication of Foxl2 as the putative primary sex-determining gene in a lizard (Anguimorpha; Shinisauridae).

Although sex determination is ubiquitous in vertebrates, mechanisms of sex determination vary from environmentally to genetically influenced. In vertebrates, genetic sex determination is typically accomplished with sex chromosomes. Groups like mammals maintain conserved sex chromosome systems, while sex chromosomes in most vertebrate clades are not conserved across similar evolutionary timescales. One group inferred to have an evolutionarily stable mode of sex determination is Anguimorpha, a clade of charismatic taxa including monitor lizards, Gila monsters, and crocodile lizards. The common ancestor of extant anguimorphs possessed a ZW system that has been retained across the clade. However, the sex chromosome system in the endangered, monotypic family of crocodile lizards (Shinisauridae) has remained elusive. Here, we analyze genomic data to demonstrate that Shinisaurus has replaced the ancestral anguimorph ZW system on LG7 with a novel ZW system on LG3. The linkage group, LG3, corresponds to chromosome 9 in chicken, and this is the first documented use of this syntenic block as a sex chromosome in amniotes. Additionally, this ~1 Mb region harbors approximately 10 genes, including a duplication of the sex-determining transcription factor, Foxl2, critical for the determination and maintenance of sexual differentiation in vertebrates, and thus a putative primary sex-determining gene for Shinisaurus.

Multi-path decoder U-Net: A weakly trained real-time segmentation network for object detection and localization in ultrasound scans.

Detecting and localizing an anatomical structure of interest within the field of view of an ultrasound scan is an essential step in many diagnostic and therapeutic procedures. However, ultrasound scans suffer from high levels of variabilities across sonographers and patients, making it challenging for sonographers to accurately identify and locate these structures without extensive experience. Segmentation-based convolutional neural networks (CNNs) have been proposed as a solution to assist sonographers in this task. Despite their accuracy, these networks require pixel-wise annotations for training; an expensive and labor-intensive operation that requires the expertise of an experienced practitioner to identify the precise outline of the structures of interest. This complicates, delays, and increases the cost of network training and deployment. To address this problem, we propose a multi-path decoder U-Net architecture that is trained on bounding box segmentation maps; not requiring pixel-wise annotations. We show that the network can be trained on small training sets, which is the case in medical imaging datasets; reducing the cost and time needed for deployment and use in clinical settings. The multi-path decoder design allows for better training of deeper layers and earlier attention to the target anatomical structures of interest. This architecture offers up to a 7% relative improvement compared to the U-Net architecture in localization and detection performance, with an increase of only 0.75% in the number of parameters. Its performance is on par with, or slightly better than, the more computationally expensive U-Net++, which has 20% more parameters; making the proposed architecture a more computationally efficient alternative for real-time object detection and localization in ultrasound scans.

Efficient Registration for Human Surfaces via Isometric Regularization on Embedded Deformation.

3D registration is a fundamental step to obtain the correspondences between surfaces. Traditional mesh alignment methods tackle this problem through non-rigid deformation, mostly accomplished by applying ICP-based (Iterative Closest Point) optimization. The embedded deformation method is proposed for the purpose of acceleration, which enables various real-time applications. However, it regularizes on an underlying simplified structure, which could be problematic for intricate cases when the simplified graph doesn't fully represent the surface attributes. Moreover, without elaborate parameter-tuning, deformation usually performs suboptimally, leading to slow convergence or a local minimum if all regions on the surface are assumed to share the same rigidity during the optimization. In this article, we propose a novel solution that decouples regularization from the underlying deformation model by explicitly managing the rigidity of vertex clusters. We further design an efficient two-step solution that alternates between isometric deformation and embedded deformation with cluster-based regularization. Our method can easily support region-adaptive regularization with cluster refinement and execute efficiently. Extensive experiments demonstrate the effectiveness of our approach for mesh alignment tasks even under large-scale deformation and imperfect data. Our method outperforms state-of-the-art methods both numerically and visually.

Self-Supervised Rigid Registration for Multimodal Retinal Images.

The ability to accurately overlay one modality retinal image to another is critical in ophthalmology. Our previous framework achieved the state-of-the-art results for multimodal retinal image registration. However, it requires human-annotated labels due to the supervised approach of the previous work. In this paper, we propose a self-supervised multimodal retina registration method to alleviate the burdens of time and expense to prepare for training data, that is, aiming to automatically register multimodal retinal images without any human annotations. Specially, we focus on registering color fundus images with infrared reflectance and fluorescein angiography images, and compare registration results with several conventional and supervised and unsupervised deep learning methods. From the experimental results, the proposed self-supervised framework achieves a comparable accuracy comparing to the state-of-the-art supervised learning method in terms of registration accuracy and Dice coefficient.

Two-Step Registration on Multi-Modal Retinal Images via Deep Neural Networks.

Multi-modal retinal image registration plays an important role in the ophthalmological diagnosis process. The conventional methods lack robustness in aligning multi-modal images of various imaging qualities. Deep-learning methods have not been widely developed for this task, especially for the coarse-to-fine registration pipeline. To handle this task, we propose a two-step method based on deep convolutional networks, including a coarse alignment step and a fine alignment step. In the coarse alignment step, a global registration matrix is estimated by three sequentially connected networks for vessel segmentation, feature detection and description, and outlier rejection, respectively. In the fine alignment step, a deformable registration network is set up to find pixel-wise correspondence between a target image and a coarsely aligned image from the previous step to further improve the alignment accuracy. Particularly, an unsupervised learning framework is proposed to handle the difficulties of inconsistent modalities and lack of labeled training data for the fine alignment step. The proposed framework first changes multi-modal images into a same modality through modality transformers, and then adopts photometric consistency loss and smoothness loss to train the deformable registration network. The experimental results show that the proposed method achieves state-of-the-art results in Dice metrics and is more robust in challenging cases.

JOINT MOTION CORRECTION AND 3D SEGMENTATION WITH GRAPH-ASSISTED NEURAL NETWORKS FOR RETINAL OCT.

Optical Coherence Tomography (OCT) is a widely used non-invasive high resolution 3D imaging technique for biological tissues and plays an important role in ophthalmology. OCT retinal layer segmentation is a fundamental image processing step for OCT-Angiography projection, and disease analysis. A major problem in retinal imaging is the motion artifacts introduced by involuntary eye movements. In this paper, we propose neural networks that jointly correct eye motion and retinal layer segmentation utilizing 3D OCT information, so that the segmentation among neighboring B-scans would be consistent. The experimental results show both visual and quantitative improvements by combining motion correction and 3D OCT layer segmentation comparing to conventional and deep-learning based 2D OCT layer segmentation.

A CNN Segmentation-Based Approach to Object Detection and Tracking in Ultrasound Scans with Application to the Vagus Nerve Detection.

Ultrasound scanning is essential in several medical diagnostic and therapeutic applications. It is used to visualize and analyze anatomical features and structures that influence treatment plans. However, it is both labor intensive, and its effectiveness is operator dependent. Real-time accurate and robust automatic detection and tracking of anatomical structures while scanning would significantly impact diagnostic and therapeutic procedures to be consistent and efficient. In this paper, we propose a deep learning framework to automatically detect and track a specific anatomical target structure in ultrasound scans. Our framework is designed to be accurate and robust across subjects and imaging devices, to operate in real-time, and to not require a large training set. It maintains a localization precision and recall higher than 90% when trained on training sets that are as small as 20% in size of the original training set. The framework backbone is a weakly trained segmentation neural network based on U-Net. We tested the framework on two different ultrasound datasets with the aim to detect and track the Vagus nerve, where it outperformed current state-of-the-art real-time object detection networks.Clinical Relevance-The proposed approach provides an accurate method to detect and localize target anatomical structures in real-time, assisting sonographers during ultrasound scanning sessions by reducing diagnostic and detection errors, and expediting the duration of scanning sessions.

Perspective Distortion Correction for Multi-Modal Registration between Ultra-Widefield and Narrow-Angle Retinal Images.

Multi-modal retinal image registration between 2D Ultra-Widefield (UWF) and narrow-angle (NA) images has not been well-studied, since most existing methods mainly focus on NA image alignment. The stereographic projection model used in UWF imaging causes strong distortions in peripheral areas, which leads to inferior alignment quality. We propose a distortion correction method that remaps the UWF images based on estimated camera view points of NA images. In addition, we set up a CNN-based registration pipeline for UWF and NA images, which consists of the distortion correction method and three networks for vessel segmentation, feature detection and matching, and outlier rejection. Experimental results on our collected dataset shows the effectiveness of the proposed pipeline and the distortion correction method.

Robust Content-Adaptive Global Registration for Multimodal Retinal Images Using Weakly Supervised Deep-Learning Framework.

Multimodal retinal imaging plays an important role in ophthalmology. We propose a content-adaptive multimodal retinal image registration method in this paper that focuses on the globally coarse alignment and includes three weakly supervised neural networks for vessel segmentation, feature detection and description, and outlier rejection. We apply the proposed framework to register color fundus images with infrared reflectance and fluorescein angiography images, and compare it with several conventional and deep learning methods. Our proposed framework demonstrates a significant improvement in robustness and accuracy reflected by a higher success rate and Dice coefficient compared with other methods.

LEARNING TO CORRECT AXIAL MOTION IN OCT FOR 3D RETINAL IMAGING.

Optical Coherence Tomography (OCT) is a powerful technique for non-invasive 3D imaging of biological tissues at high resolution that has revolutionized retinal imaging. A major challenge in OCT imaging is the motion artifacts introduced by involuntary eye movements. In this paper, we propose a convolutional neural network that learns to correct axial motion in OCT based on a single volumetric scan. The proposed method is able to correct large motion, while preserving the overall curvature of the retina. The experimental results show significant improvements in visual quality as well as overall error compared to the conventional methods in both normal and disease cases.

Learning Image Profile Enhancement and Denoising Statistics Priors for Single-Image Super-Resolution.

Single-image super-resolution (SR) has been widely used in computer vision applications. The reconstruction-based SR methods are mainly based on certain prior terms to regularize the SR problem. However, it is very challenging to further improve the SR performance by the conventional design of explicit prior terms. Because of the powerful learning ability, deep convolutional neural networks (CNNs) have been widely used in single-image SR task. However, it is difficult to achieve further improvement by only designing the network architecture. In addition, most existing deep CNN-based SR methods learn a nonlinear mapping function to directly map low-resolution (LR) images to desirable high-resolution (HR) images, ignoring the observation models of input images. Inspired by the split Bregman iteration (SBI) algorithm, which is a powerful technique for solving the constrained optimization problems, the original SR problem is divided into two subproblems: 1) inversion subproblem and 2) denoising subproblem. Since the inversion subproblem can be regarded as an inversion step to reconstruct an intermediate HR image with sharper edges and finer structures, we propose to use deep CNN to capture low-level explicit image profile enhancement prior (PEP). Since the denoising subproblem aims to remove the noise in the intermediate image, we adopt a simple and effective denoising network to learn implicit image denoising statistics prior (DSP). Furthermore, the penalty parameter in SBI is adaptively tuned during the iterations for better performance. Finally, we also prove the convergence of our method. Thus, the deep CNNs are exploited to capture both implicit and explicit image statistics priors. Due to SBI, the SR observation model is also leveraged. Consequently, it bridges between two popular SR approaches: 1) learning-based method and 2) reconstruction-based method. Experimental results show that the proposed method achieves the state-of-the-art SR results.

Out of the Hengduan Mountains: Molecular phylogeny and historical biogeography of the Asian water snake genus Trimerodytes (Squamata: Colubridae).

The Asian water snake genus Trimerodytes (formerly Sinonatrix) is endemic to East and Southeast Asia. Although several species have been included in various phylogenetic studies previously, the evolution and relationships among members of this genus as a whole remain unexplored. In this study, we report the sequencing two protein-coding mitochondrial gene fragments (MTCYB and ND2) and three nuclear genes (c-mos, NT3, and Rag1), reconstruct interspecific phylogeny, and explore biogeography for the genus Trimerodytes. Both Bayesian inference and maximum likelihood analyses consistently recover the monophyly of Trimerodytes with strong support, with T. yapingi the sister-group to the remaining species. The divergence date and ancestral area estimation suggest that Trimerodytes likely originated in Hengduan Mountains (eastern Tibetan Plateau) in western China at 23.93 Ma (95% HPD: 17.09-31.30), and intraspecific divergence began at about 4.23 Ma (95% HPD: 2.74-6.10). Analyses support the validity of T. yunnanensis.

Boosting Feature Matching Accuracy with Pairwise Affine Estimation.

Local image feature matching lies in the heart of many computer vision applications. Achieving high matching accuracy is challenging when significant geometric difference exists between the source and target images. The traditional matching pipeline addresses the geometric difference by introducing the concept of support region. Around each feature point, the support region defines a neighboring area characterized by estimated attributes like scale, orientation, affine shape, etc. To correctly assign support region is not an easy job, especially when each feature is processed individually. In this paper, we propose to estimate the relative affine transformation for every pair of to-be-compared features. This "tailored" measurement of geometric difference is more precise and helps improve the matching accuracy. Our pipeline can be incorporated into most existing 2D local image feature detectors and descriptors. We comprehensively evaluate its performance with various experiments on a diversified selection of benchmark datasets. The results show that the majority of tested detectors/descriptors gain additional matching accuracy with proposed pipeline.

A new species of Lycodon Boie, 1826 (Serpentes, Colubridae) from northern Vietnam.

A new species of the genus Lycodon is described from Cao Bang Province, Vietnam, based on three individuals with distinct differences in morphology and molecular data. The new species is differentiated from its congeners by a combination of the following characters: dorsal scales in 17-17-15 rows, smooth throughout; supralabials usually eight (rarely nine); infralabials ten; one elongated loreal on each side, in contact with the eye; precloacal plate single; ventral scales 212-218 (plus one or two preventral scales); subcaudals 90 or 91; maxillary teeth 13 or 14; dorsal surface of body with 28 or 29 light body bands; dorsal surface of tail with 13 cream bands, forming a distinct blotch in the vertebral region. Based on phylogenetic analyses of mitochondrial cytochrome b sequence data, the new species is recovered as the sister species to a clade containing L. multizonatus and L. liuchengchaoi with strong support from the Bayesian analysis. The new species is at least 7.5% divergent from other species within this clade in uncorrected pairwise distance calculated using a fragment of more than 1000 bp of the mitochondrial cytochrome b. This discovery increases the number of Lycodon species known from Vietnam to 16.

High ISO JPEG Image Denoising by Deep Fusion of Collaborative and Convolutional Filtering.

Capturing images at high ISO modes will introduce much realistic noise, which is difficult to be removed by traditional denoising methods. In this paper, we propose a novel denoising method for high ISO JPEG images via deep fusion of collaborative and convolutional filtering. Collaborative filtering explores the non-local similarity of natural images, while convolutional filtering takes advantage of the large capacity of convolutional neural networks (CNNs) to infer noise from noisy images. We observe that the noise variance map of a high ISO JPEG image is spatial-dependent and has a Bayer-like pattern. Therefore, we introduce the Bayer pattern prior in our noise estimation and collaborative filtering stages. Since collaborative filtering is good at recovering repeatable structures and convolutional filtering is good at recovering irregular patterns and removing noise in flat regions, we propose to fuse the strengths of the two methods via deep CNN. The experimental results demonstrate that our method outperforms the state-of-the-art realistic noise removal methods for a wide variety of testing images in both subjective and objective measurements. In addition, we construct a dataset with noisy and clean image pairs for high ISO JPEG images to facilitate research on this topic.

Random Forest with Learned Representations for Semantic Segmentation.

We present a random forest framework that learns the weights, shapes, and sparsities of feature representations for real-time semantic segmentation. Typical filters (kernels) have predetermined shapes and sparsities and learn only weights. A few feature extraction methods fix weights and learn only shapes and sparsities. These predetermined constraints restrict learning and extracting optimal features. To overcome this limitation, we propose an unconstrained representation that is able to extract optimal features by learning weights, shapes, and sparsities. We, then, present the random forest framework that learns the flexible filters using an iterative optimization algorithm and segments input images using the learned representations. We demonstrate the effectiveness of the proposed method using a hand segmentation dataset for hand-object interaction and using two semantic segmentation datasets. The results show that the proposed method achieves real-time semantic segmentation using limited computational and memory resources.

Enhanced Non-Local Total Variation Model and Multi-Directional Feature Prediction Prior for Single Image Super Resolution.

It is widely acknowledged that single image super-resolution (SISR) methods play a critical role in recovering the missing high-frequencies in an input low-resolution image. As SISR is severely ill-conditioned, image priors are necessary to regularize the solution spaces and generate the corresponding high-resolution image. In this paper, we propose an effective SISR framework based on the enhanced non-local similarity modeling and learning-based multi-directional feature prediction (ENLTV-MDFP). Since both the modeled and learned priors are exploited, the proposed ENLTV-MDFP method benefits from the complementary properties of the reconstruction-based and learning-based SISR approaches. Specifically, for the non-local similarity-based modeled prior [enhanced non-local total variation, (ENLTV)], it is characterized via the decaying kernel and stable group similarity reliability schemes. For the learned prior [multi-directional feature prediction prior, (MDFP)], it is learned via the deep convolutional neural network. The modeled prior performs well in enhancing edges and suppressing visual artifacts, while the learned prior is effective in hallucinating details from external images. Combining these two complementary priors in the MAP framework, a combined SR cost function is proposed. Finally, the combined SR problem is solved via the split Bregman iteration algorithm. Based on the extensive experiments, the proposed ENLTV-MDFP method outperforms many state-of-the-art algorithms visually and quantitatively.

Accelerating GMM-Based Patch Priors for Image Restoration: Three Ingredients for a Speed-Up.

Image restoration methods aim to recover the underlying clean image from corrupted observations. The expected patch log-likelihood (EPLL) algorithm is a powerful image restoration method that uses a Gaussian mixture model (GMM) prior on the patches of natural images. Although it is very effective for restoring images, its high runtime complexity makes the EPLL ill-suited for most practical applications. In this paper, we propose three approximations to the original EPLL algorithm. The resulting algorithm, which we call the fast-EPLL (FEPLL), attains a dramatic speed-up of two orders of magnitude over EPLL while incurring a negligible drop in the restored image quality (less than 0.5 dB). We demonstrate the efficacy and versatility of our algorithm on a number of inverse problems, such as denoising, deblurring, super-resolution, inpainting, and devignetting. To the best of our knowledge, the FEPLL is the first algorithm that can competitively restore a pixel image in under 0.5 s for all the degradations mentioned earlier without specialized code optimizations, such as CPU parallelization or GPU implementation.

Morphological and molecular review of Jacob's Mountain Stream Keelback Opisthotropis jacobi Angel Bourret, 1933 (Squamata: Natricidae) with description of a sibling species from northern Vietnam.

New morphological data including hemipenis morphology is provided for Opisthotropis jacobi, a poorly known Mountain Stream Keelback from Vietnam and China, based on three newly collected individuals from Sa Pa (Lao Cai Province) and Tam Dao (Vinh Phuc Province) in northern Vietnam. In addition, morphological data from Vietnam is summarized based on the original description (Angel Bourret 1933), on the overview book by Bourret (1936) and on a number of smaller, little-known contributions by the latter author along with re-examination of specimens deposited in the herpetological collection of the Muséum national d'Histoire naturelle, Paris. We also sequenced a fragment of the mitochondrial cytochrome b from the newly collected specimens of the Jacob's Mountain Stream Keelback and performed molecular analyses of new and existing data of the genus. A recently discovered Opisthotropis population from Tay Yen Tu Nature Reserve in Bac Giang Province, northern Vietnam, which at the first glance resembled O. jacobi morphologically, is shown to diverge both genetically and morphologically from the existing species and is herein described as a new species. Opisthotropis voquyi sp. nov. is characterized by the combination of the following characters: internasal not in contact with loreal; one preocular; usually two postoculars; one anterior temporal; one posterior temporal; 7 or 8, rarely 9 supralabials; 25 maxillary teeth; subcaudals 74-86; 15 dorsal scale rows at neck, at midbody and before vent; body scales smooth or only with few faint keels; and dorsal scales being greyish, greyish-brown or brown in preservative, posteriorly more or less edged with pale greyish-brown. Phylogenetically, the new species is supported as a sister taxon to O. jacobi, but the two taxa are approximately 10% divergent based on cytochrome b data.