MOTIVATION: Recent brain mapping efforts are producing large-scale whole-brain images using different imaging modalities. Accurate alignment and delineation of anatomical structures in these images are essential for numerous studies. These requirements are typically modeled as two distinct tasks: registration and segmentation. However, prevailing methods, fail to fully explore and utilize the inherent correlation and complementarity between the two tasks. Furthermore, variations in brain anatomy, brightness, and texture pose another formidable challenge in designing multi-modal similarity metrics. A high-throughput approach capable of overcoming the bottleneck of multi-modal similarity metric design, while effective leveraging the highly correlated and complementary nature of two tasks is highly desirable. RESULTS: We introduce a deep learning framework for joint registration and segmentation of multi-modal brain images. Under this framework, registration and segmentation tasks are deeply coupled and collaborated at two hierarchical layers. In the inner layer, we establish a strong feature-level coupling between the two tasks by learning a unified common latent feature representation. In the outer layer, we introduce a mutually supervised dual-branch network to decouple latent features and facilitate task-level collaboration between registration and segmentation. Since the latent features we designed are also modality-independent, the bottleneck of designing multi-modal similarity metric is essentially addressed. Another merit offered by this framework is the interpretability of latent features, which allows intuitive manipulation of feature learning, thereby further enhancing network training efficiency and the performance of both tasks. Extensive experiments conducted on both multi-modal and mono-modal datasets of mouse and human brains demonstrate the superiority of our method. AVAILABILITY AND IMPLEMENTATION: The code is available at https://github.com/tingtingup/DCRS.

MOTIVATION: Accurate segmentation and recognition of C.elegans cells are critical for various biological studies, including gene expression, cell lineages, and cell fates analysis at single-cell level. However, the highly dense distribution, similar shapes, and inhomogeneous intensity profiles of whole-body cells in 3D fluorescence microscopy images make automatic cell segmentation and recognition a challenging task. Existing methods either rely on additional fiducial markers or only handle a subset of cells. Given the difficulty or expense associated with generating fiducial features in many experimental settings, a marker-free approach capable of reliably segmenting and recognizing C.elegans whole-body cells is highly desirable. RESULTS: We report a new pipeline, called automated segmentation and recognition (ASR) of cells, and applied it to 3D fluorescent microscopy images of L1-stage C.elegans with 558 whole-body cells. A novel displacement vector field based deep learning model is proposed to address the problem of reliable segmentation of highly crowded cells with blurred boundary. We then realize the cell recognition by encoding and exploiting statistical priors on cell positions and structural similarities of neighboring cells. To the best of our knowledge, this is the first method successfully applied to the segmentation and recognition of C.elegans whole-body cells. The ASR-segmentation module achieves an F1-score of 0.8956 on a dataset of 116 C.elegans image stacks with 64 728 cells (accuracy 0.9880, AJI 0.7813). Based on the segmentation results, the ASR recognition module achieved an average accuracy of 0.8879. We also show ASR's applicability to other cell types, e.g. platynereis and rat kidney cells. AVAILABILITY AND IMPLEMENTATION: The code is available at https://github.com/reaneyli/ASR.

Most of the existing photoplethysmography (PPG)-based blood pressure (BP) estimation methods aim at training a general BP model applicable to all individuals which neglected the vasculature and anatomical differences among individuals as well as the slow and subtle cardiovascular changes over time, thus, were hard to achieve high accuracy. This study aims at addressing this problem by constructing personalized BP models from PPG signals. First, the PPG features that can well reflect the changes of individual BP with the physiological state were extracted. Afterwards, an error feedback incremental support vector regression (EFISVR) model was designed to achieve high-accuracy BP measurement of a subject, which can quickly be adapted to new samples without retraining the whole model. Results show that the constructed model can accurately predict the BP values of a subject for at least three months. The mean absolute error (MAE) of BP estimation were 3.11 mmHg for systolic BP (SBP) and 2.47 mmHg for diastolic BP (DBP). The proposed EFISVR model is lightweight which can be integrated into wearables and other edge devices, as a part of Internet of Things (IoT) applications. The advantages of lightweight, few-shot learning and high precision make the model suitable for applications in real-life scenarios.

Hyperspectral images (HISs) have continuous spectra that can be used to accurately identify the land cover contained within them. Generally speaking, small-scale spectral features can reveal local information within the spectral sequence, while large-scale ones capture global information across spectra. Recently, the proposed Transformer-based hyperspectral image classification (HIC) methods have shown superiority over convolutional neural networks (CNNs) in processing long spectral sequences but struggle with extracting local spectral details. Meanwhile, the multiscale information of the spectral dimension has not been fully explored. To address these issues, we propose a multiscale spatial–spectral Transformer named LSDnet. First, a bilateral filtering-based feature enhancement (BFFE) module is utilized to promote the reliability of shallow features. Then, a long- and short-distance spatial–spectral crossattention (LSDSC) module is proposed to capture both local and global HSI features. Moreover, a multiscale spectral embedding (MSSE) module is introduced to enrich local details among adjacent spectral bands. To enhance the model generalization, the spectral position bias (SPB) coding is designed in the Transformer encoder, adapting to variable spectral numbers of different HSIs. Furthermore, the traditional data partitioning strategy for HIC suffers from information leakage. Therefore, we propose a new data partitioning method to prevent the overlapping between the training and testing data. Meanwhile, an adjustment factor is utilized to balance the number of samples in each category, while a dynamic equilibrium loss (DEL) function is proposed to ensure the training contribution of every category. Experiments on three public datasets validate the rationality and effectiveness of our proposed HIC method. The codes will be available at:https://gitee.com/pdypdy/lsdsc_-tgrs.git.

In this work, we propose a voxel-based single-stage fine-grained and efficient point cloud 3D object detection algorithm to address the inadequate granularity in point cloud feature extraction tasks and the imbalance between efficiency and accuracy in single-stage point cloud 3D object detection scenarios. We develop a lightweight multibranch cross-sparse convolution network (LMCCN) that is designed to preserve the feature granularity of the original point cloud while achieving enhanced extraction efficiency. Additionally, we introduce a compact fine-grained self-attention augmented bird’s eye view (BEV) feature extraction module (CFSAM). This module aims to further refine BEV features, enabling the acquisition of both locally and globally enhanced features and thereby augmentingthe perceptual capabilities of the constructed model. Without bells and whistles, the proposed method attains excellent performance on many autonomous driving benchmarks, with detection accuracies of up to 81.67% on KITTI, 72.74% on ONCE, and 84.00% on nuScenes. Moreover, it reaches a peak detection speed of 46.08 FPS, effectively balancing accuracy with speed.

The main differences in images of footprints are the proportion of the parts of foot and the distribution of pressure, which can be considered as fine-grained image classification. Moreover, the deviation of human body weight and muscle strength increases the difficulty of identifying the left and right feet. While using a fine-grained image classification network to solve the footprint image classification problem is certainly a feasible approach, the number of parameters in a fine-grained image classification network is generally large, and therefore we would like to build a lightweight classification network that is suitable for several small footprint datasets. In this paper, a multimodal footprint recognition algorithm based on progressive multi-granularity feature fusion is proposed. First, the shallow dense connection network is used to extract features. The feature extraction ability of the model is improved with the help of channel splicing and feature multiplexing. Second, to learn footprint images of different granularities, the progressive training strategy and puzzle scrambler are applied to the model. Finally, factorized bilinear coding can aggregate local features to obtain more discriminative global representation features. Experiments show that our network achieves comparable classification accuracy to some fine-grained image classification models (PMG, MSEC) on the complete pressure footprint dataset, but the number of parameters in our network is greatly reduced. Meanwhile, our network also achieves good classification results on several other footprint datasets, which demonstrates the effectiveness of our network. At the same time, an ablation experiment was carried out to verify the effectiveness of the progressive strategy and the factorized bilinear coding.

SUMMARY: Recent whole-brain mapping projects are collecting increasingly larger sets of high-resolution brain images using a variety of imaging, labeling and sample preparation techniques. Both mining and analysis of these data require reliable and robust cross-modal registration tools. We recently developed the mBrainAligner, a pipeline for performing cross-modal registration of the whole mouse brain. However, using this tool requires scripting or command-line skills to assemble and configure the different modules of mBrainAligner for accommodating different registration requirements and platform settings. In this application note, we present mBrainAligner-Web, a web server with a user-friendly interface that allows to configure and run mBrainAligner locally or remotely across platforms. AVAILABILITY AND IMPLEMENTATION: mBrainAligner-Web is available at http://mbrainaligner.ahu.edu.cn/ with source code at https://github.com/reaneyli/mBrainAligner-web. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.