66 research outputs found
High-quality Panorama Stitching based on Asymmetric Bidirectional Optical Flow
In this paper, we propose a panorama stitching algorithm based on asymmetric
bidirectional optical flow. This algorithm expects multiple photos captured by
fisheye lens cameras as input, and then, through the proposed algorithm, these
photos can be merged into a high-quality 360-degree spherical panoramic image.
For photos taken from a distant perspective, the parallax among them is
relatively small, and the obtained panoramic image can be nearly seamless and
undistorted. For photos taken from a close perspective or with a relatively
large parallax, a seamless though partially distorted panoramic image can also
be obtained. Besides, with the help of Graphics Processing Unit (GPU), this
algorithm can complete the whole stitching process at a very fast speed:
typically, it only takes less than 30s to obtain a panoramic image of
9000-by-4000 pixels, which means our panorama stitching algorithm is of high
value in many real-time applications. Our code is available at
https://github.com/MungoMeng/Panorama-OpticalFlow.Comment: Published at the 5th International Conference on Computational
Intelligence and Applications (ICCIA 2020
Spiking Inception Module for Multi-layer Unsupervised Spiking Neural Networks
Spiking Neural Network (SNN), as a brain-inspired approach, is attracting
attention due to its potential to produce ultra-high-energy-efficient hardware.
Competitive learning based on Spike-Timing-Dependent Plasticity (STDP) is a
popular method to train an unsupervised SNN. However, previous unsupervised
SNNs trained through this method are limited to a shallow network with only one
learnable layer and cannot achieve satisfactory results when compared with
multi-layer SNNs. In this paper, we eased this limitation by: 1)We proposed a
Spiking Inception (Sp-Inception) module, inspired by the Inception module in
the Artificial Neural Network (ANN) literature. This module is trained through
STDP-based competitive learning and outperforms the baseline modules on
learning capability, learning efficiency, and robustness. 2)We proposed a
Pooling-Reshape-Activate (PRA) layer to make the Sp-Inception module stackable.
3)We stacked multiple Sp-Inception modules to construct multi-layer SNNs. Our
algorithm outperforms the baseline algorithms on the hand-written digit
classification task, and reaches state-of-the-art results on the MNIST dataset
among the existing unsupervised SNNs.Comment: Published at the 2020 International Joint Conference on Neural
Networks (IJCNN); Extended from arXiv:2001.0168
Merging-Diverging Hybrid Transformer Networks for Survival Prediction in Head and Neck Cancer
Survival prediction is crucial for cancer patients as it provides early
prognostic information for treatment planning. Recently, deep survival models
based on deep learning and medical images have shown promising performance for
survival prediction. However, existing deep survival models are not well
developed in utilizing multi-modality images (e.g., PET-CT) and in extracting
region-specific information (e.g., the prognostic information in Primary Tumor
(PT) and Metastatic Lymph Node (MLN) regions). In view of this, we propose a
merging-diverging learning framework for survival prediction from
multi-modality images. This framework has a merging encoder to fuse
multi-modality information and a diverging decoder to extract region-specific
information. In the merging encoder, we propose a Hybrid Parallel
Cross-Attention (HPCA) block to effectively fuse multi-modality features via
parallel convolutional layers and cross-attention transformers. In the
diverging decoder, we propose a Region-specific Attention Gate (RAG) block to
screen out the features related to lesion regions. Our framework is
demonstrated on survival prediction from PET-CT images in Head and Neck (H&N)
cancer, by designing an X-shape merging-diverging hybrid transformer network
(named XSurv). Our XSurv combines the complementary information in PET and CT
images and extracts the region-specific prognostic information in PT and MLN
regions. Extensive experiments on the public dataset of HEad and neCK TumOR
segmentation and outcome prediction challenge (HECKTOR 2022) demonstrate that
our XSurv outperforms state-of-the-art survival prediction methods.Comment: Early Accepted at International Conference on Medical Image Computing
and Computer Assisted Intervention (MICCAI 2023
AutoFuse: Automatic Fusion Networks for Deformable Medical Image Registration
Deformable image registration aims to find a dense non-linear spatial
correspondence between a pair of images, which is a crucial step for many
medical tasks such as tumor growth monitoring and population analysis.
Recently, Deep Neural Networks (DNNs) have been widely recognized for their
ability to perform fast end-to-end registration. However, DNN-based
registration needs to explore the spatial information of each image and fuse
this information to characterize spatial correspondence. This raises an
essential question: what is the optimal fusion strategy to characterize spatial
correspondence? Existing fusion strategies (e.g., early fusion, late fusion)
were empirically designed to fuse information by manually defined prior
knowledge, which inevitably constrains the registration performance within the
limits of empirical designs. In this study, we depart from existing
empirically-designed fusion strategies and develop a data-driven fusion
strategy for deformable image registration. To achieve this, we propose an
Automatic Fusion network (AutoFuse) that provides flexibility to fuse
information at many potential locations within the network. A Fusion Gate (FG)
module is also proposed to control how to fuse information at each potential
network location based on training data. Our AutoFuse can automatically
optimize its fusion strategy during training and can be generalizable to both
unsupervised registration (without any labels) and semi-supervised registration
(with weak labels provided for partial training data). Extensive experiments on
two well-benchmarked medical registration tasks (inter- and intra-patient
registration) with eight public datasets show that our AutoFuse outperforms
state-of-the-art unsupervised and semi-supervised registration methods.Comment: Under Revie
Non-iterative Coarse-to-fine Transformer Networks for Joint Affine and Deformable Image Registration
Image registration is a fundamental requirement for medical image analysis.
Deep registration methods based on deep learning have been widely recognized
for their capabilities to perform fast end-to-end registration. Many deep
registration methods achieved state-of-the-art performance by performing
coarse-to-fine registration, where multiple registration steps were iterated
with cascaded networks. Recently, Non-Iterative Coarse-to-finE (NICE)
registration methods have been proposed to perform coarse-to-fine registration
in a single network and showed advantages in both registration accuracy and
runtime. However, existing NICE registration methods mainly focus on deformable
registration, while affine registration, a common prerequisite, is still
reliant on time-consuming traditional optimization-based methods or extra
affine registration networks. In addition, existing NICE registration methods
are limited by the intrinsic locality of convolution operations. Transformers
may address this limitation for their capabilities to capture long-range
dependency, but the benefits of using transformers for NICE registration have
not been explored. In this study, we propose a Non-Iterative Coarse-to-finE
Transformer network (NICE-Trans) for image registration. Our NICE-Trans is the
first deep registration method that (i) performs joint affine and deformable
coarse-to-fine registration within a single network, and (ii) embeds
transformers into a NICE registration framework to model long-range relevance
between images. Extensive experiments with seven public datasets show that our
NICE-Trans outperforms state-of-the-art registration methods on both
registration accuracy and runtime.Comment: Accepted at International Conference on Medical Image Computing and
Computer Assisted Intervention (MICCAI 2023
DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CT
Nasopharyngeal Carcinoma (NPC) is a malignant epithelial cancer arising from
the nasopharynx. Survival prediction is a major concern for NPC patients, as it
provides early prognostic information to plan treatments. Recently, deep
survival models based on deep learning have demonstrated the potential to
outperform traditional radiomics-based survival prediction models. Deep
survival models usually use image patches covering the whole target regions
(e.g., nasopharynx for NPC) or containing only segmented tumor regions as the
input. However, the models using the whole target regions will also include
non-relevant background information, while the models using segmented tumor
regions will disregard potentially prognostic information existing out of
primary tumors (e.g., local lymph node metastasis and adjacent tissue
invasion). In this study, we propose a 3D end-to-end Deep Multi-Task Survival
model (DeepMTS) for joint survival prediction and tumor segmentation in
advanced NPC from pretreatment PET/CT. Our novelty is the introduction of a
hard-sharing segmentation backbone to guide the extraction of local features
related to the primary tumors, which reduces the interference from non-relevant
background information. In addition, we also introduce a cascaded survival
network to capture the prognostic information existing out of primary tumors
and further leverage the global tumor information (e.g., tumor size, shape, and
locations) derived from the segmentation backbone. Our experiments with two
clinical datasets demonstrate that our DeepMTS can consistently outperform
traditional radiomics-based survival prediction models and existing deep
survival models.Comment: Accepted at IEEE Journal of Biomedical and Health Informatics (JBHI
Intrinsic Cerebro-Cerebellar Functional Connectivity Reveals the Function of Cerebellum VI in Reading-Related Skills
Funding This work was supported by grants from the National Natural Science Foundation of China (NSFC: 31971036, 31971039, and 31571158).Peer reviewedPublisher PD
Effect of 60Co-γ Irradiation on Postharvest Physiology and Lipid Nutrition of Fresh Hazelnuts
Fresh hazelnuts were treated by 60Co-γ irradiation (0, 0.25, 0.50, 0.75 and 1.00 kGy) and stored at (4.0 ± 0.5) ℃ for up to three months. Changes in physiological indexes and lipid nutrition were monitored during the storage period. The results showed that irradiation at a dose of 0.25–1.00 kGy delayed the decline in superoxide dismutase (SOD) and catalase (CAT) activity, and reduced the activity of polyphenol oxidase (PPO) in fresh hazelnuts. The irradiation dose of 0.50 kGy was the most effective, and at the end of storage, the respiratory intensity, malondialdehyde (MDA) content and lipoxygenase activity of the irradiated sample decreased by 25.81%, 18.50% and 4.18% compared with those of the non-irradiated one, respectively. However, irradiation had no significant effect on fatty acid composition and content, peroxide value (POV) or acid value (AV). Principal component analysis (PCA) performed on fresh hazelnuts stored for 90 d also showed that 0.50 kGy 60Co-γ irradiation imparted the best storage quality to fresh hazelnuts. These results suggest that 60Co-γ irradiation can delay the senescence and effectively extend the shelf life by affecting the postharvest physiology of fresh hazelnuts
Prediction of 5-year progression-free survival in advanced nasopharyngeal carcinoma with pretreatment PET/CT using multi-modality deep learning-based radiomics
ObjectiveDeep learning-based radiomics (DLR) has achieved great success in medical image analysis and has been considered a replacement for conventional radiomics that relies on handcrafted features. In this study, we aimed to explore the capability of DLR for the prediction of 5-year progression-free survival (PFS) in advanced nasopharyngeal carcinoma (NPC) using pretreatment PET/CT images.MethodsA total of 257 patients (170/87 patients in internal/external cohorts) with advanced NPC (TNM stage III or IVa) were enrolled. We developed an end-to-end multi-modality DLR model, in which a 3D convolutional neural network was optimized to extract deep features from pretreatment PET/CT images and predict the probability of 5-year PFS. The TNM stage, as a high-level clinical feature, could be integrated into our DLR model to further improve the prognostic performance. For a comparison between conventional radiomics and DLR, 1,456 handcrafted features were extracted, and optimal conventional radiomics methods were selected from 54 cross-combinations of six feature selection methods and nine classification methods. In addition, risk group stratification was performed with clinical signature, conventional radiomics signature, and DLR signature.ResultsOur multi-modality DLR model using both PET and CT achieved higher prognostic performance (area under the receiver operating characteristic curve (AUC) = 0.842 ± 0.034 and 0.823 ± 0.012 for the internal and external cohorts) than the optimal conventional radiomics method (AUC = 0.796 ± 0.033 and 0.782 ± 0.012). Furthermore, the multi-modality DLR model outperformed single-modality DLR models using only PET (AUC = 0.818 ± 0.029 and 0.796 ± 0.009) or only CT (AUC = 0.657 ± 0.055 and 0.645 ± 0.021). For risk group stratification, the conventional radiomics signature and DLR signature enabled significant difference between the high- and low-risk patient groups in both the internal and external cohorts (p < 0.001), while the clinical signature failed in the external cohort (p = 0.177).ConclusionOur study identified potential prognostic tools for survival prediction in advanced NPC, which suggests that DLR could provide complementary values to the current TNM staging
Responses of sequential and hierarchical phenological events to warming and cooling in alpine meadows
Organisms' life cycles consist of hierarchical stages, from a single phenological stage (for example, flowering within a season), to vegetative and reproductive phases, to the total lifespan of the individual. Yet phenological events are typically studied in isolation, limiting our understanding of life history responses to climate change. Here, we reciprocally transfer plant communities along an elevation gradient to investigate plastic changes in the duration of sequential phenological events for six alpine species. We show that prolonged flowering leads to longer reproductive phases and activity periods when plants are moved to warmer locations. In contrast, shorter post-fruiting leaf and flowering stages led to shorter vegetative and reproductive phases, respectively, which resulted in shorter activity periods when plants were moved to cooler conditions. Therefore, phenological responses to warming and cooling do not simply mirror one another in the opposite direction, and low temperature may limit reproductive allocation in the alpine region
- …