A complete plant body consists of elements on different scales, including microscopic molecules, mesoscopic multicellular structures, and macroscopic tissues and organs, which are interconnected to form complex biological networks. The growth and development of plants involve the regulation of elements on different scales and their biological networks, which requires the coordinated operation of multiple molecules, cells, tissues, and organs. It is difficult to reveal the essence of multi-level life activities by a single method or technology. In recent years, the development of various novel imaging technologies has provided new approaches for revealing the complex life activities in plants. Using multi-modal imaging technologies to study the cross-scale network connections of plants from the microscopic, mesoscopic, and macroscopic levels is crucial for understanding the complex internal connections behind biological functions. This paper first summarizes multi-modal cross-scale imaging technologies, three-dimensional reconstruction, and image processing methods, outlines the basic framework of cross-scale network connection properties, and then summarizes the applications of multi-modal imaging technologies in elucidating plant multi-scale networks. Finally, this review systematically integrates the combined analysis of cross-scale 3D spatial structural data and single-cell omics, laying a theoretical foundation for the innovation of novel plant imaging technologies. Furthermore, it provides a new research paradigm for in-depth exploration of the interaction mechanisms among cross-scale elements and the principles of biological network connectivity in plant life activities.
Plants/metabolism* ; Imaging, Three-Dimensional/methods* ; Image Processing, Computer-Assisted/methods* ; Multimodal Imaging/methods* ; Plant Physiological Phenomena

Accurately obtaining the crop quantity and density is not only crucial for the demand-based input of water and fertilizer in the field but also vital for ensuring the yield and quality of crops. Aerial photography by unmanned aerial vehicles (UAVs) can quickly acquire the distribution image information of crops over a large area. However, the accurate recognition of a single type of dense targets is a huge challenge for most recognition algorithms. Taking banana seedlings as an example in this study, we captured the images of banana plantations by UAVs from high altitudes to explore an efficient recognition method for dense targets. We proposed a strategy of "cut-recognition-stitch" and constructed a counting method based on the improved Faster R-CNN algorithm. First, the images containing highly dense targets were cropped into a large number of image tiles according to different sizes (simulating different flight altitudes), and the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm was adopted to improve the image quality. A banana seedling dataset containing 36 000 image tiles was constructed. Then, the Faster R-CNN network with optimized parameters was used to train the banana seedling recognition model. Finally, the recognition results were reversely stitched together, and a boundary deduplication algorithm was designed to correct the final counting results to reduce the repeated recognition caused by image cropping. The results show that the recognition accuracy of the Faster R-CNN with optimized parameters for banana image datasets of different sizes can reach up to 0.99 at most. The deduplication algorithm can reduce the average counting error for the original aerial images from 1.60% to 0.60%, and the average counting accuracy of banana seedlings reaches 99.4%. The proposed method effectively addresses the challenge of recognizing dense small objects in high-resolution aerial images, providing an efficient and reliable technical solution for intelligent crop density monitoring in precision agriculture.
Musa/growth & development* ; Crops, Agricultural/growth & development* ; Algorithms ; Neural Networks, Computer ; Unmanned Aerial Devices ; Seedlings/growth & development* ; Image Processing, Computer-Assisted/methods* ; Photography ; Agriculture/methods*

Objective To explore the application value of multi-model adaptive statistical iterative reconstruction-Veo (ASiR-V) in ultra-low dose chest CT examination of children in the plateau area. Methods The children who underwent chest CT examination in Xizang Autonomous Region People's Hospital were enrolled in this study and assigned into two groups according to the scanning conditions.Group A underwent scanning at a tube voltage of 100 kV and ASiR-V 50% reconstruction,and group B underwent scanning at a tube voltage of 80 kV and ASiR-V 0 (Group B1) and ASiR-V 50% (Group B2) reconstruction.The image quality of each group was evaluated objectively and subjectively.The radiation dose and image quality were compared between groups. Results Groups A and B showed the volume CT dose indexes of (2.33±0.62) mGy and (0.86±0.01) mGy and the dose length products of (65.01±25.12) mGy·cm and (23.55±3.38) mGy·cm,respectively,which presented differences between groups (both P<0.001).The image noise in the bilateral upper and middle lung areas in group B2 was lower than that in group B1 but higher than that in group A (all P<0.001).There was no significant difference in image quality score of the lung window among groups (all P>0.05).Groups A,B1,and B2 had no significant differences in ascending aorta (P=0.538) or liver CT value (P=0.175) in the mediastinal window.The signal-to-noise ratios and contrast-to-noise ratios of ascending aorta and liver in group B2 were higher than those in group B1 (all P<0.001) and lower than those in group A (all P<0.05).The image quality score of the mediastinal window followed a descending order of group A>group B2>group B1 (all P<0.001)。Conclusion ASiR-V combined with low tube voltage can effectively reduce the radiation dose and guarantee the image quality of chest CT of children in the plateau area.
Humans ; Radiation Dosage ; Tomography, X-Ray Computed/methods* ; Child ; Male ; Female ; Child, Preschool ; Radiography, Thoracic/methods* ; Infant ; Models, Statistical ; Image Processing, Computer-Assisted/methods*

Objective To develop a diagnostic model combining the CT angiography(CCTA)-derived myocardial radiomics signatures with the CT-derived fractional flow reserve(CT-FFR)based on coronary CCTA and investigate the diagnostic accuracy of the hybrid model for hemodynamically significant coronary artery disease(CAD).Methods The patients presenting stable angina pectoris,diagnosed with CAD,and clinically referred for CCTA examination and invasive coronary angiography were prospectively recruited.Radiomics features of the left ventricular myocardium were extracted from the three main perfusion territories demarcated according to the coronary blood supply.The extracted features were first selected by the minimum redundancy maximum relevance feature ranking method.A least absolute shrinkage and selection operator Logistic regression algorithm with leave-one-out cross-validation was then employed to construct a radiomics model.The CT-FFR value was generated for each blood vessel.The area under the receiver operating characteristics curve(AUC_ROC),sensitivity,and specificity were adopted to evaluate the performance of each model against the reference standard invasive coronary angiography/FFR.Results A total of 70 patients[42 men and 28 women;(61±10) years old] were included in this study and complemented CCTA examination,with 175 vessels and the corresponding myocardial territories undergoing invasive coronary angiography/FFR.A total of 1 656 specific radiomics parameters were extracted,from which 14 features were selected to establish the radiomics model.The AUC_ROC,sensitivity,and specificity were 0.797(95%CI=0.732-0.861),77.1%,and 73.7%for the radiomics model,0.892(95%CI=0.841-0.943),81.4%,and 88.8%for the CT-FFR model,and 0.928(95%CI=0.890-0.965),83.3%,and 88.4%for the hybrid model,respectively.The hybrid model outperformed the radiomics model and CT-FFR alone(P=0.040).Conclusions The radiomics signatures of the vessel-related myocardium from CCTA could provide incremental value to the diagnostic performance of CT-FFR and improve vessel-specific ischemia detection.The hybrid model combining CT-FFR with radiomics signatures is potentially feasible for improving the diagnostic accuracy for hemodynamically significant CAD.
Coronary Angiography/methods* ; Tomography, X-Ray Computed ; Humans ; Hemodynamics ; Coronary Artery Disease/diagnostic imaging* ; Male ; Female ; Middle Aged ; Aged ; Radiomics ; Angina Pectoris/diagnostic imaging* ; China ; Image Processing, Computer-Assisted ; Coronary Vessels/diagnostic imaging*

OBJECTIVE: To compare the registration accuracy of cone beam computed tomography (CBCT) images while registering to virtual craniodentofacial patients based on soft tissue and the dentition registration method. METHODS: Virtual dentofacial patients out of 13 selected participants who needed CBCT scanning were established by impression with a registered-block impression (RBI) based on digital dental images, three-dimensional (3D) facial images and maxillofacial CBCT images. CBCT images were processed in the Mimics software program, establishing the craniofacial virtual patients based on CBCT images (CCTs). Registration between virtual patients from RBI and CCT, using the soft tissue in lower half face (STE) and dentition (DTN) as the reference area, respectively, forming two kinds of virtual craniofacial patients based on digital dental images, 3D facial images and skeletal images of CBCT (hiding the soft tissue and dental casts from CBCT). Three-dimensional deviation analysis was performed in the upper half face and lower half face of facial images from CBCT between two kinds of virtual craniodentofacial patients and compared with 3D facial images from RBI and recorded as root mean square error (RMSE). Paired-t test was used to compare the deviations of RMSEs between the upper and lower half of the face and the upper half of the face of facial images from CCT, respectively, between the two kinds of virtual craniodentofacial patients based on STE and DTN methods. RESULTS: Paired-t tests showed that there was no statistically significant difference between the upper and lower half faces of facial images from CCT between STE and DTN (P>0.05), but the deviation of RMSEs of the upper half face of facial images from CCT in STE was smaller than those in DTN [(1.696±0.420) mm vs. (1.752±0.424) mm, P < 0.01]. CONCLUSION The registration accuracy of CBCT registered in virtual craniodentofacial patients using soft tissue as the reference area was higher.
Humans ; Cone-Beam Computed Tomography/methods* ; Imaging, Three-Dimensional/methods* ; Male ; Face/anatomy & histology* ; Female ; Adult ; Image Processing, Computer-Assisted/methods* ; Young Adult ; User-Computer Interface

With the development of artificial intelligence technology and the growing demand for minimally invasive surgery, the intelligentization of minimally invasive surgery has become a current research hotspot. Surgical instrument segmentation is a highly promising technology that can enhance the performance of minimally invasive endoscopic imaging systems, surgical video analysis systems, and other related systems. This article summarizes the semantic and instance segmentation methods of minimally invasive surgical instruments based on deep learning, deeply analyzes the supervision methods of training algorithms, network structure improvements, and attention mechanisms, and then discusses the methods based on the Segment Anything Model. Given that deep learning methods have extremely high requirements for data, current data augmentation methods have also been explored. Finally, a summary and outlook on instrument segmentation technology are provided.
Artificial Intelligence ; Minimally Invasive Surgical Procedures/instrumentation* ; Algorithms ; Deep Learning ; Humans ; Image Processing, Computer-Assisted

The clinical application of binocular endoscope relies primarily on the visual system of physicians to create a three-dimensional effect, but it cannot provide accurate depth information. The utilization of 3D reconstruction technology in binocular endoscopy can facilitate the recovery of image depth information, and the application of deep learning-based 3D reconstruction technology can significantly improve the accuracy and real-time performance of reconstruction results, making it widely applicable in the realm of minimally invasive surgery. This paper aims to explore the key technologies and implementation methods of deep learning based 3D reconstruction for binocular endoscopic images, and seeks to outline strategies for enhancing the quality of 3D reconstruction in endoscopic images, providing guidance for sustainable development of binocular endoscopic image reconstruction technology in clinical settings. This will assist in the application of minimally invasive surgery and contribute to meeting the demands of precision medicine.
Deep Learning ; Imaging, Three-Dimensional/methods* ; Humans ; Endoscopy/methods* ; Image Processing, Computer-Assisted/methods* ; Minimally Invasive Surgical Procedures

Computer-assisted methods for pathological image analysis can improve doctor's efficiency of image reading and diagnostic accuracy, effectively addressing the shortage of pathology diagnostic manpower. With the rapid development of artificial intelligence and digital pathology, deep learning technology has spurred a wealth of research in the field of histopathology. This article reviews the various applications of deep learning in digital pathological image analysis, such as pathological image segmentation, cancer auxiliary diagnosis, and cancer prognosis prediction, and discusses the challenges and solutions in its application. Furthermore, it predicts future trends in deep learning for pathological image analysis and proposes potential research directions.
Deep Learning ; Humans ; Image Processing, Computer-Assisted/methods* ; Artificial Intelligence ; Neoplasms

The internal structures of cells as the basic units of life are a major wonder of the microscopic world. Cellular images provide an intriguing window to help explore and understand the composition and function of these structures. Scientific imagery combined with artistic expression can further expand the potential of imaging in educational dissemination and interdisciplinary applications. This study presents an innovative diffusion model-based approach for style transfer in cellular images, combining scientific rigor with artistic expression. By leveraging training-free large-scale pre-trained diffusion models, the proposed method integrates the intricate morphological and textural features of cellular images with diverse artistic styles. Key techniques such as the inversion of denoising diffusion implicit models (DDIMs), adaptive instance normalization (AdaIN), self-attention style injection, and attention temperature scaling ensure the preservation of cellular structures while enhancing visual expressiveness. The results showcase the potential of this strategy for interdisciplinary applications, enriching both the visualization and educational dissemination of cellular imagery through compelling storytelling and aesthetic appeal.
Humans ; Image Processing, Computer-Assisted/methods* ; Cells ; Diffusion

OBJECTIVES: We present a new low-dose CT reconstruction method using sub-pixel and anisotropic diffusion. METHODS: The sub-pixel intensity values and their second-order differences were obtained using linear interpolation techniques, and the new gradient information was then embedded into an anisotropic diffusion process, which was introduced into a penalty-weighted least squares model to reduce the noise in low-dose CT projection data. The high-quality CT image was finally reconstructed using the classical filtered back-projection (FBP) algorithm from the estimated data. RESULTS: In the Shepp-Logan phantom experiments, the structural similarity (SSIM) index of the CT image reconstructed by the proposed algorithm, as compared with FBP, PWLS-Gibbs and PWLS-TV algorithms, was increased by 28.13%, 5.49%, and 0.91%, the feature similarity (FSIM) index was increased by 21.08%, 1.78%, and 1.36%, and the root mean square error (RMSE) was reduced by 69.59%, 18.96%, and 3.90%, respectively. In the digital XCAT phantom experiments, the SSIM index of the CT image reconstructed by the proposed algorithm, as compared with FBP, PWLS-Gibbs and PWLS-TV algorithms, was increased by 14.24%, 1.43% and 7.89%, the FSIM index was increased by 9.61%, 1.78% and 5.66%, and the RMSE was reduced by 26.88%, 9.41% and 18.39%, respectively. In clinical experiments, the SSIM index of the image reconstructed using the proposed algorithm was increased by 19.24%, 15.63% and 3.68%, the FSIM index was increased by 4.30%, 2.92% and 0.43%, and the RMSE was reduced by 44.60%, 36.84% and 15.22% in comparison with FBP, PWLS-Gibbs and PWLS-TV algorithms, respectively. CONCLUSIONS The proposed method can effectively reduce the noises and artifacts while maintaining the structural details in low-dose CT images.
Tomography, X-Ray Computed/methods* ; Algorithms ; Phantoms, Imaging ; Anisotropy ; Image Processing, Computer-Assisted/methods* ; Humans ; Radiation Dosage