VASCULAR ARCHITECTURE OF THE HUMAN OPTIC CHIASMA AND BITEMPORAL HEMIANOPIA

The blood supply system of the optic chiasma was studied in 85 fresh human specimens using various histological and anatomical methods. Computer image analysis and ultrastructural examinations of the microvessel in 58fetal specimens were also conducted. The authors found that the medial portion of the chiasma is a weak point in the microcirculation network. This weak point is apt to be disturbed first and become ischemic. causing disorders of the crossing optic nerve fibers and resulting in characterestic bitemporal visual field defects. SEM studies showed no ultrastructural difference between the capillaries at the medial and lateral portions of the chiasma. It was concluded that: 1) No special artery supplies the median chiasma the weak point of microcirculation at the median chiasma is due to its relatively scanty capillary distribution; 2) 'lateral chiasma arteries' could provide a better blood supply to the lateral fibers and thus the nasal quadrantic visual field could be preserved in many late stages of visual field defect in sellar region tumors, 3) cases with pituitary microadenoma which is not sufficiently large to press the chiasma but involves bitemporal visual field defect are due to the tumor recieving “shunt-flow” (stealing blood) from the chiasma through the peri-infundibulum plexus.

A Study on Ionospheric HF Channel with Bitemporal Response and Scattering Function

At first the bitemporal response method is introduced to solve the scattering function of the ionospeeric channel. We can get the scattering function, as a function, of the group path time delay and Doppler frequency. Thus Doppler effect resulting from the continuous movement of the ionosphere is analyzed to study the characteristics of the various ionospheric irregularities and diturbance. many possible problems and correction are researched lastly.

Deep Feature Extraction and Feature Fusion for Bi-Temporal Satellite Image Classification

Multispectral images contain a large amount of spatial and spectral data which are effective in identifying change areas.Deep feature extraction is important for multispectral image classification and is evolving as an interesting research area in change detection.However,many deep learning framework based approaches do not consider both spatial and textural details into account.In order to handle this issue,a Convolutional Neural Network(CNN)based multi-feature extraction and fusion is introduced which considers both spatial and textural features.This method uses CNN to extract the spatio-spectral features from individual channels and fuse them with the textural features.Then the fused image is classified into change and unchanged regions.The presence of mixed pixels in the bitemporal satellite images affect the classification accuracy due to the misclassification errors.The proposed method was compared with six state-of-theart change detection methods and analyzed.The main highlight of this method is that by taking into account the spatio-spectral and textural information in the input channels,the mixed pixel problem is solved.Experiments indicate the effectiveness of this method and demonstrate that it possesses low misclassification errors,higher overall accuracy and kappa coefficient.

A transformer-based Siamese network and an open optical dataset for semantic change detection of remote sensing images

Recent change detection(CD)methods focus on the extraction of deep change semantic features.However,existing methods overlook the fine-grained features and have the poor ability to capture long-range space–time information,which leads to the micro changes missing and the edges of change types smoothing.In this paper,a potential transformer-based semantic change detection(SCD)model,Pyramid-SCDFormer is proposed,which precisely recognizes the small changes and fine edges details of the changes.The SCD model selectively merges different semantic tokens in multi-head self-attention block to obtain multiscale features,which is crucial for extraction information of remote sensing images(RSIs)with multiple changes from different scales.Moreover,we create a well-annotated SCD dataset,Landsat-SCD with unprecedented time series and change types in complex scenarios.Comparing with three Convolutional Neural Network-based,one attention-based,and two transformer-based networks,experimental results demonstrate that the Pyramid-SCDFormer stably outperforms the existing state-of-the-art CD models and obtains an improvement in MIoU/F1 of 1.11/0.76%,0.57/0.50%,and 8.75/8.59%on the LEVIR-CD,WHU_CD,and Landsat-SCD dataset respectively.For change classes proportion less than 1%,the proposed model improves the MIoU by 7.17–19.53%on Landsat-SCD dataset.The recognition performance for small-scale and fine edges of change types has greatly improved.

Biomechanics of human optic chiasmal compression: ex vivo experiment and finite element modelling

The mechanism of bitemporal hemianopia arising as a result of chiasmal compression is unknown.In this study,we combined an ex vivo experiment and finite element modelling(FEM)to investigate its potential mechanism.A cadaveric human optic chiasm was scanned using micro-CT before and after deformation by inflation of Foley catheter,to simulate tumour growth from beneath.The geometry of the same chiasm was reconstructed and simulated using finite element analysis.Chiasmal deformations were extracted from the simulation and compared with those observed during micro-CT scanning.In addition,nerve fibre models examining variation in local fibre distribution patterns of the chiasm were incorporated to investigate the strain(deformation)distributions of the chiasm at an axonal level.The FEM model matched the micro-CT scans well both qualitatively and quantitatively.Compression of the chiasm induced high strains in the paracentral portions of the chiasm where the crossing optic nerve fibres are located.At an axonal level,the magnitude of strains affecting crossed fibres were greater than those affecting uncrossed fibres.The high strains in the paracentral portions of the chiasm,combined with the differences in strain between crossed and uncrossed nerve fibres,are consistent with a biomechanical explanation for the pattern of visual field loss seen in chiasmal compression.