Objective:To explore the usefulness of multishot diffusion tensor imaging(DTI)for evaluating the neurological function of patients with spinal cord tumors Methods:Routine magnetic resonance imaging and multishot DTI w...Objective:To explore the usefulness of multishot diffusion tensor imaging(DTI)for evaluating the neurological function of patients with spinal cord tumors Methods:Routine magnetic resonance imaging and multishot DTI were performed in five patients with spinal cord tumors.The values of fractional anisotropy(FA)and radial diffusivity(RD)were analyzed.Results:Multishot DTI of spinal cord tumors allowed for defining the margins of tumors and determining the relationship of tumors with the adjacent white matter structures of the spinal cord.Multishot DTI demonstrated significantly increased RD and decreased FA of spinal cord tumors compared with those of the normal spinal cord.Conclusions:Multishot DTI is a potentially useful modality for differentiating resectable tumors from nonresectable ones based on preoperative imaging alone as well as for differentiating intramedullary tumors from extramedullary ones.Further prospective studies are warranted to confirm these results.展开更多
IC(Image Captioning)is a crucial part of Visual Data Processing and aims at understanding for providing captions that verbalize an image’s important elements.However,in existing works,because of the complexity in ima...IC(Image Captioning)is a crucial part of Visual Data Processing and aims at understanding for providing captions that verbalize an image’s important elements.However,in existing works,because of the complexity in images,neglecting major relation between the object in an image,poor quality image,labelling it remains a big problem for researchers.Hence,the main objective of this work attempts to overcome these challenges by proposing a novel framework for IC.So in this research work the main contribution deals with the framework consists of three phases that is image understanding,textual understanding and decoding.Initially,the image understanding phase is initiated with image pre-pro-cessing to enhance image quality.Thereafter,object has been detected using IYV3MMDs(Improved YoloV3 Multishot Multibox Detectors)in order to relate the interrelation between the image and the object,and then it is followed by MBFOCNNs(Modified Bacterial Foraging Optimization in Convolution Neural Networks),which encodes and providesfinal feature vectors.Secondly,the tex-tual understanding phase is performed based on an image which is initiated with preprocessing of text where unwanted words,phrases,punctuations are removed in order to provide a healthy text.It is then followed by MGloVEs(Modified Glo-bal Vectors for Word Representation),which provides a word embedding of fea-tures with the highest priority towards the object present in an image.Finally,the decoding phase has been performed,which decodes the image whether it may be a normal or complex scene image and provides an accurate text by its learning ability using MDAA(Modified Deliberate Adaptive Attention).The experimental outcome of this work shows better accuracy of shows 96.24%when compared to existing and similar methods while generating captions for images.展开更多
文摘Objective:To explore the usefulness of multishot diffusion tensor imaging(DTI)for evaluating the neurological function of patients with spinal cord tumors Methods:Routine magnetic resonance imaging and multishot DTI were performed in five patients with spinal cord tumors.The values of fractional anisotropy(FA)and radial diffusivity(RD)were analyzed.Results:Multishot DTI of spinal cord tumors allowed for defining the margins of tumors and determining the relationship of tumors with the adjacent white matter structures of the spinal cord.Multishot DTI demonstrated significantly increased RD and decreased FA of spinal cord tumors compared with those of the normal spinal cord.Conclusions:Multishot DTI is a potentially useful modality for differentiating resectable tumors from nonresectable ones based on preoperative imaging alone as well as for differentiating intramedullary tumors from extramedullary ones.Further prospective studies are warranted to confirm these results.
文摘IC(Image Captioning)is a crucial part of Visual Data Processing and aims at understanding for providing captions that verbalize an image’s important elements.However,in existing works,because of the complexity in images,neglecting major relation between the object in an image,poor quality image,labelling it remains a big problem for researchers.Hence,the main objective of this work attempts to overcome these challenges by proposing a novel framework for IC.So in this research work the main contribution deals with the framework consists of three phases that is image understanding,textual understanding and decoding.Initially,the image understanding phase is initiated with image pre-pro-cessing to enhance image quality.Thereafter,object has been detected using IYV3MMDs(Improved YoloV3 Multishot Multibox Detectors)in order to relate the interrelation between the image and the object,and then it is followed by MBFOCNNs(Modified Bacterial Foraging Optimization in Convolution Neural Networks),which encodes and providesfinal feature vectors.Secondly,the tex-tual understanding phase is performed based on an image which is initiated with preprocessing of text where unwanted words,phrases,punctuations are removed in order to provide a healthy text.It is then followed by MGloVEs(Modified Glo-bal Vectors for Word Representation),which provides a word embedding of fea-tures with the highest priority towards the object present in an image.Finally,the decoding phase has been performed,which decodes the image whether it may be a normal or complex scene image and provides an accurate text by its learning ability using MDAA(Modified Deliberate Adaptive Attention).The experimental outcome of this work shows better accuracy of shows 96.24%when compared to existing and similar methods while generating captions for images.
