Free form deformation and symmetry constraint‐based multimodal brain image registration using generative adversarial nets

Multi‐modal brain image registration has been widely applied to functional localisation,neurosurgery and computational anatomy.The existing registration methods based on the dense deformation fields involve too many parameters,which is not conducive to the exploration of correct spatial correspondence between the float and reference images.Meanwhile,the unidirectional registration may involve the deformation folding,which will result in the change of topology during registration.To address these issues,this work has presented an unsupervised image registration method using the free form deformation(FFD)and the symmetry constraint‐based generative adversarial networks(FSGAN).The FSGAN utilises the principle component analysis network‐based structural representations of the reference and float images as the inputs and uses the generator to learn the FFD model parameters,thereby producing two deformation fields.Meanwhile,the FSGAN uses two discriminators to decide whether the bilateral registration have been realised simultaneously.Besides,the symmetry constraint is utilised to construct the loss function,thereby avoiding the deformation folding.Experiments on BrainWeb,high grade gliomas,IXI and LPBA40 show that compared with state‐of‐the‐art methods,the FSGAN provides superior performance in terms of visual comparisons and such quantitative indexes as dice value,target registration error and computational efficiency.

B^(2)C^(3)NetF^(2):Breast cancer classification using an end‐to‐end deep learning feature fusion and satin bowerbird optimization controlled Newton Raphson feature selection

Currently,the improvement in AI is mainly related to deep learning techniques that are employed for the classification,identification,and quantification of patterns in clinical images.The deep learning models show more remarkable performance than the traditional methods for medical image processing tasks,such as skin cancer,colorectal cancer,brain tumour,cardiac disease,Breast cancer(BrC),and a few more.The manual diagnosis of medical issues always requires an expert and is also expensive.Therefore,developing some computer diagnosis techniques based on deep learning is essential.Breast cancer is the most frequently diagnosed cancer in females with a rapidly growing percentage.It is estimated that patients with BrC will rise to 70%in the next 20 years.If diagnosed at a later stage,the survival rate of patients with BrC is shallow.Hence,early detection is essential,increasing the survival rate to 50%.A new framework for BrC classification is presented that utilises deep learning and feature optimization.The significant steps of the presented framework include(i)hybrid contrast enhancement of acquired images,(ii)data augmentation to facilitate better learning of the Convolutional Neural Network(CNN)model,(iii)a pre‐trained ResNet‐101 model is utilised and modified according to selected dataset classes,(iv)deep transfer learning based model training for feature extraction,(v)the fusion of features using the proposed highly corrected function‐controlled canonical correlation analysis approach,and(vi)optimal feature selection using the modified Satin Bowerbird Optimization controlled Newton Raphson algorithm that finally classified using 10 machine learning classifiers.The experiments of the proposed framework have been carried out using the most critical and publicly available dataset,such as CBISDDSM,and obtained the best accuracy of 94.5%along with improved computation time.The comparison depicts that the presented method surpasses the current state‐ofthe‐art approaches.

APPLICATION VALUE OF MAGNETIC RESONANCE SEQUENCES IN DIAGNOSIS OF EARLY SPINAL METASTATIC TUMOR

Objective To investigate the clinical value of different magnetic resonance (MR) pulse sequences in diagnosis of spinal metastatic tumor. Methods Fifteen patients with clinically suspected spinal metastatic tumor were included in this study. These patients were with documented primary tumors. Four MR pulse sequences, T1-weighted spin echo (T1WI SE), T2-weighted fast spin echo (T2WI FSE), short time inversion recovery (STIR), and gradient echo 2-D multi echo data imaging combination (GE Me-2D) were used to detect spinal metastasis. Results Fifteen vertebral bodies were entire involvement, 38 vertebral bodies were section involvement, and totally 53 vertebral bodies were involved. There were 19 focal infections in pedicle of vertebral arch, 15 metastases in spinous process and transverse process. Fifty-three vertebral bodies were abnormal in T1WI SE and GE Me-2D, 35 vertebral bodies were found abnormal in T2WI FSE, and 50 vertebral bodies were found abnormal in STIR. The verges of focal signal of involved vertebral bodies were comparatively clear in T1WI SE, comparatively clear or vague in T2WI FSE, vague in STIR, and clear in GE Me-2D.Conclusions GE Me-2D may be the most sensitive technique to detect metastases. So three sequences (T1WI SE, T2WI FSE, GE Me-2D) can demonstrate the early changes of spinal metastasis roundly.