45 research outputs found
PHE-SICH-CT-IDS: A Benchmark CT Image Dataset for Evaluation Semantic Segmentation, Object Detection and Radiomic Feature Extraction of Perihematomal Edema in Spontaneous Intracerebral Hemorrhage
Intracerebral hemorrhage is one of the diseases with the highest mortality
and poorest prognosis worldwide. Spontaneous intracerebral hemorrhage (SICH)
typically presents acutely, prompt and expedited radiological examination is
crucial for diagnosis, localization, and quantification of the hemorrhage.
Early detection and accurate segmentation of perihematomal edema (PHE) play a
critical role in guiding appropriate clinical intervention and enhancing
patient prognosis. However, the progress and assessment of computer-aided
diagnostic methods for PHE segmentation and detection face challenges due to
the scarcity of publicly accessible brain CT image datasets. This study
establishes a publicly available CT dataset named PHE-SICH-CT-IDS for
perihematomal edema in spontaneous intracerebral hemorrhage. The dataset
comprises 120 brain CT scans and 7,022 CT images, along with corresponding
medical information of the patients. To demonstrate its effectiveness,
classical algorithms for semantic segmentation, object detection, and radiomic
feature extraction are evaluated. The experimental results confirm the
suitability of PHE-SICH-CT-IDS for assessing the performance of segmentation,
detection and radiomic feature extraction methods. To the best of our
knowledge, this is the first publicly available dataset for PHE in SICH,
comprising various data formats suitable for applications across diverse
medical scenarios. We believe that PHE-SICH-CT-IDS will allure researchers to
explore novel algorithms, providing valuable support for clinicians and
patients in the clinical setting. PHE-SICH-CT-IDS is freely published for
non-commercial purpose at:
https://figshare.com/articles/dataset/PHE-SICH-CT-IDS/23957937
ECPC-IDS:A benchmark endometrail cancer PET/CT image dataset for evaluation of semantic segmentation and detection of hypermetabolic regions
Endometrial cancer is one of the most common tumors in the female
reproductive system and is the third most common gynecological malignancy that
causes death after ovarian and cervical cancer. Early diagnosis can
significantly improve the 5-year survival rate of patients. With the
development of artificial intelligence, computer-assisted diagnosis plays an
increasingly important role in improving the accuracy and objectivity of
diagnosis, as well as reducing the workload of doctors. However, the absence of
publicly available endometrial cancer image datasets restricts the application
of computer-assisted diagnostic techniques.In this paper, a publicly available
Endometrial Cancer PET/CT Image Dataset for Evaluation of Semantic Segmentation
and Detection of Hypermetabolic Regions (ECPC-IDS) are published. Specifically,
the segmentation section includes PET and CT images, with a total of 7159
images in multiple formats. In order to prove the effectiveness of segmentation
methods on ECPC-IDS, five classical deep learning semantic segmentation methods
are selected to test the image segmentation task. The object detection section
also includes PET and CT images, with a total of 3579 images and XML files with
annotation information. Six deep learning methods are selected for experiments
on the detection task.This study conduct extensive experiments using deep
learning-based semantic segmentation and object detection methods to
demonstrate the differences between various methods on ECPC-IDS. As far as we
know, this is the first publicly available dataset of endometrial cancer with a
large number of multiple images, including a large amount of information
required for image and target detection. ECPC-IDS can aid researchers in
exploring new algorithms to enhance computer-assisted technology, benefiting
both clinical doctors and patients greatly.Comment: 14 pages,6 figure
Coupling Artificial Neurons in BERT and Biological Neurons in the Human Brain
Linking computational natural language processing (NLP) models and neural
responses to language in the human brain on the one hand facilitates the effort
towards disentangling the neural representations underpinning language
perception, on the other hand provides neurolinguistics evidence to evaluate
and improve NLP models. Mappings of an NLP model's representations of and the
brain activities evoked by linguistic input are typically deployed to reveal
this symbiosis. However, two critical problems limit its advancement: 1) The
model's representations (artificial neurons, ANs) rely on layer-level
embeddings and thus lack fine-granularity; 2) The brain activities (biological
neurons, BNs) are limited to neural recordings of isolated cortical unit (i.e.,
voxel/region) and thus lack integrations and interactions among brain
functions. To address those problems, in this study, we 1) define ANs with
fine-granularity in transformer-based NLP models (BERT in this study) and
measure their temporal activations to input text sequences; 2) define BNs as
functional brain networks (FBNs) extracted from functional magnetic resonance
imaging (fMRI) data to capture functional interactions in the brain; 3) couple
ANs and BNs by maximizing the synchronization of their temporal activations.
Our experimental results demonstrate 1) The activations of ANs and BNs are
significantly synchronized; 2) the ANs carry meaningful linguistic/semantic
information and anchor to their BN signatures; 3) the anchored BNs are
interpretable in a neurolinguistic context. Overall, our study introduces a
novel, general, and effective framework to link transformer-based NLP models
and neural activities in response to language and may provide novel insights
for future studies such as brain-inspired evaluation and development of NLP
models
AATCT-IDS: A Benchmark Abdominal Adipose Tissue CT Image Dataset for Image Denoising, Semantic Segmentation, and Radiomics Evaluation
Methods: In this study, a benchmark \emph{Abdominal Adipose Tissue CT Image
Dataset} (AATTCT-IDS) containing 300 subjects is prepared and published.
AATTCT-IDS publics 13,732 raw CT slices, and the researchers individually
annotate the subcutaneous and visceral adipose tissue regions of 3,213 of those
slices that have the same slice distance to validate denoising methods, train
semantic segmentation models, and study radiomics. For different tasks, this
paper compares and analyzes the performance of various methods on AATTCT-IDS by
combining the visualization results and evaluation data. Thus, verify the
research potential of this data set in the above three types of tasks.
Results: In the comparative study of image denoising, algorithms using a
smoothing strategy suppress mixed noise at the expense of image details and
obtain better evaluation data. Methods such as BM3D preserve the original image
structure better, although the evaluation data are slightly lower. The results
show significant differences among them. In the comparative study of semantic
segmentation of abdominal adipose tissue, the segmentation results of adipose
tissue by each model show different structural characteristics. Among them,
BiSeNet obtains segmentation results only slightly inferior to U-Net with the
shortest training time and effectively separates small and isolated adipose
tissue. In addition, the radiomics study based on AATTCT-IDS reveals three
adipose distributions in the subject population.
Conclusion: AATTCT-IDS contains the ground truth of adipose tissue regions in
abdominal CT slices. This open-source dataset can attract researchers to
explore the multi-dimensional characteristics of abdominal adipose tissue and
thus help physicians and patients in clinical practice. AATCT-IDS is freely
published for non-commercial purpose at:
\url{https://figshare.com/articles/dataset/AATTCT-IDS/23807256}.Comment: 17 pages, 7 figure
Chat2Brain: A Method for Mapping Open-Ended Semantic Queries to Brain Activation Maps
Over decades, neuroscience has accumulated a wealth of research results in
the text modality that can be used to explore cognitive processes.
Meta-analysis is a typical method that successfully establishes a link from
text queries to brain activation maps using these research results, but it
still relies on an ideal query environment. In practical applications, text
queries used for meta-analyses may encounter issues such as semantic redundancy
and ambiguity, resulting in an inaccurate mapping to brain images. On the other
hand, large language models (LLMs) like ChatGPT have shown great potential in
tasks such as context understanding and reasoning, displaying a high degree of
consistency with human natural language. Hence, LLMs could improve the
connection between text modality and neuroscience, resolving existing
challenges of meta-analyses. In this study, we propose a method called
Chat2Brain that combines LLMs to basic text-2-image model, known as Text2Brain,
to map open-ended semantic queries to brain activation maps in data-scarce and
complex query environments. By utilizing the understanding and reasoning
capabilities of LLMs, the performance of the mapping model is optimized by
transferring text queries to semantic queries. We demonstrate that Chat2Brain
can synthesize anatomically plausible neural activation patterns for more
complex tasks of text queries.Comment: 8 pages, 4 figure
Self-Powered Flexible Sour Sensor for Detecting Ascorbic Acid Concentration Based on Triboelectrification/Enzymatic-Reaction Coupling Effect
Artificial sensory substitution systems can mimic human sensory organs through replacing the sensing process of a defective sensory receptor and transmitting the sensing signal into the nervous system. Here, we report a self-powered flexible gustation sour sensor for detecting ascorbic acid concentration. The material system comprises of Na2C2O4-Ppy with AAO modification, PDMS and Cu wire mesh. The working mechanism is contributed to the triboelectrification/enzymatic-reaction coupling effect, and the device can collect weak energy from body movements and directly output triboelectric current without any external power-units. The triboelectric output is affected by AA concentration, and the response is up to 34.82% against 15.625 mM/L of AA solution. Furthermore, a practical application in detecting ascorbic acid concentration of different drinks has been demonstrated. This work can encourage the development of wearable flexible electronics and this self-powered sour sensor has the potential that can be acted as a kind of gustatory receptors to build electronic tongues
A new Cretaceous Metatherian mammal from Henan, China
We report a new deltatheroidan mammal from the Upper Cretaceous of Henna, China. The new taxon, Lotheridium mengi, is based on a nearly complete skull and associated lower jaws with full adult dentition. Deltatheroidans are known mostly from fragmentary specimens from Asia and North America. Previous views on deltatheroidan relationships were diverse, but recent studies favored their metatherian affinity. The new specimen represents the most complete skull known for deltatheroidans and provides additional evidence that deltatheroidans already had the distinctive metatherian dental formula and replacement pattern and several other derived metatherian features, supporting the metatherian status for this clade. The new species also indicates that deltatheroidan mammals were more diverse and had broader geographical distributions than previously thought
Zinc-Based Biodegradable Materials for Orthopaedic Internal Fixation
Traditional inert materials used in internal fixation have caused many complications and generally require removal with secondary surgeries. Biodegradable materials, such as magnesium (Mg)-, iron (Fe)- and zinc (Zn)-based alloys, open up a new pathway to address those issues. During the last decades, Mg-based alloys have attracted much attention by researchers. However, the issues with an over-fast degradation rate and release of hydrogen still need to be overcome. Zn alloys have comparable mechanical properties with traditional metal materials, e.g., titanium (Ti), and have a moderate degradation rate, potentially serving as a good candidate for internal fixation materials, especially at load-bearing sites of the skeleton. Emerging Zn-based alloys and composites have been developed in recent years and in vitro and in vivo studies have been performed to explore their biodegradability, mechanical property, and biocompatibility in order to move towards the ultimate goal of clinical application in fracture fixation. This article seeks to offer a review of related research progress on Zn-based biodegradable materials, which may provide a useful reference for future studies on Zn-based biodegradable materials targeting applications in orthopedic internal fixation