Research On Semantic Segmentation Algorithm Of Cerebral Subcortical Structure Based On Deep Learning

Subcortical structure segmentation is the basis of computer-aided diagnosis and treatment of neurology and other related diseases.By segmenting and analyzing brain structures in MRI images,early diagnosis and treatment of diseases can be performed,such as autism spectrum disorders,stroke,brain tumors and etc.In order to solve the problem of accurate subcortical segmentation,this paper proposes two parts of inherited work based on the deep learning theory,namely the DenseMedic and ACNN networks,for segmenting brain anatomies of MRI images.In the first work of DenseMedic,firstly,the OreoDown method increases the growth rate of the characteristic receptive field by increasing the stride of convolutions in early layers,and uses convolutions with constant input and output sizes to restore the network depth in a sandwich-like manner,so that the increase in growth rate brings an effective receptive field increase;secondly,DenseMedic utilizes the construction theory of DenseNet to instantiate the OreoDown framework,while obtaining multi-scale context information through densely connected feature extracting layers;finally,the introduction of hybrid dilated convolutions in each layer further expands the receptive field,solving the possibly defect of rough feature extraction.In the second work of ACNN,firstly,the alternate connection through layers is proposed,and OreoDown is then instantiated as a single-path ACNN;secondly,the division of multi-path ACNN is performed in the center of the single-path ACNN,which unifies the single-modal and multi-modal segmentation tasks in the OreoDown framework without any changes in the parameter amount;finally,the priori information of singe-modal MR images is introduced to the multi-path ACNN to differentiate the gray distribution in separate paths,which further improves the segmentation performance in single-modal tasks.This paper conducted sufficient experiments on the public IBSR and MRBrainS18 datasets for subcortical segmentation,utilizing Dice Similarity Coefficient(DSC),Intersection over Union(IoU),95%Hausdorff surface distance(HSD95)and Average Surface Distance(ASD)to evaluate the segmentation performance of neural networks.Simultaneous ablation experiments on both datasets prove the effectiveness of the proposed methods.The segmenting comparisons between DenseMedic and other networks in T1-modal tasks as well as ACNN and others in T1 single-modal,T1+FLAIR bimodal and T1+T1IR bimodal tasks also proves state-of-the-art segmenting performance of DenseMedic and ACNN.The experimental results show that the segmented subcortical structures and corresponding ground truths have more overlaps in target regions and more similarity in external profiles,which indicates that DenseMedic can effectively accomplish the segmentation of major subcortical structures.In clinical applications,the presented DenseMedic will help to accurately measure the key indicators for the central nervous system related diseases and provide rapid computer-aided diagnosis and treatment.