A Partitioning-Stacking Prediction Fusion Network Based on an Improved Attention U-Net for Stroke Lesion Segmentation

Abstract

Due to the narrow time window for the treatment of acute ischemic stroke, the stroke lesion area in the patient must be identified as quickly and accurately as possible to evaluate the risks and get the most timely and effective treatment. Therefore, there is great clinical significance in the study of automatic identification and segmentation methods for stroke lesions. In this paper, we propose a partitioningstacking prediction fusion (PSPF) method based on an improved attention U-net to solve the problems of 3D-CNN-based networks, including their high computational cost and insufficient training data, and to achieve accurate segmentation of 3D stroke lesions. Our proposed PSPF method includes three steps in the first part. In Step 1, partitioning, we partition the slices obtained in a certain plane direction by slicing a Magnetic Resonance Imaging (MRI) into subsets according to the 2D graph similarity, then use each partitioned subset to perform training and prediction separately. In Step 2, stacking, we stack the 2D slice results of all subsets according to the position order in MRI before slicing and partitioning to form a 3D lesion result. In Step 3, fusion, we use soft voting to fuse the three orthogonal planes' 3D results that were obtained voxel by voxel in Steps 1 and 2. In the second part, we propose an improved attention U-net, which uses the features from three different scales to generate the attention gating coefficients that further improve training efficiency and segmentation accuracy. We implement a 6-fold cross-validation on the Anatomical Tracings of Lesions After Stroke (ATLAS) dataset to validate our method and model using metrics such as Dice Coefficient (DC), F2 score, and Area under Precision-Recall curve (APR). The results show that compared to the existing methods, our proposed method can not only improve the segmentation precision on unbalanced data but also improve the detailed performance of lesion segmentation. Our proposed method and model are generalized and accurate, demonstrating the good potential for clinical routines. The source codes and models in our method have been made publicly available at [available.upon.acceptance].

A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms

“…1 ATLAS v1.2 has been accessed and cited widely since its release in 2018, with reports including the improved performance of stroke lesion segmentation algorithms using novel methods, particularly deep learning and convolutional neural networks (e.g. [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] ).…”

“…The ATLAS v2.0 dataset was developed using similar protocols and methods as the ATLAS v1.2 dataset, which has been successfully utilized to develop numerous lesion segmentation methods for the last several years. [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] For ATLAS v2.0, detailed manual quality control for image quality occurred during the initial lesion segmentation, and all segmentations were examined for quality by two additional researchers. Following preprocessing, lesions were again checked for proper registration to template space.…”

Small Lesion Segmentation in Brain MRIs with Subpixel Embedding

“…[35] performed 3D convolutions on a subsection of the scan and fused the results with 2D convolutions. [11] proposed an attention gate to combine 2D segmentations along the axial, sagittal, coronal planes into a 3D volume. However, these works use significantly larger memory footprints and 3D convolutions are computationally expensive -limiting the models' practicality.…”

Deep Learning for Prediction of Mechanism in Acute Ischemic Stroke Using Brain MRI

“…The diffusion imaging lesion pattern, which provides useful information for early diagnosis of acute ischemic stroke, has been reported to be closely related to the stroke subtype 8,9 . To diagnose acute ischemic stroke in brain MRI images, various deep learning algorithms based on convolutional neural networks (CNNs) have been proposed [10][11][12][13][14][15][16][17][18][19][20][21] . These studies have shown that deep learning can detect stroke lesions more accurately than traditional machine learning techniques and can extract meaningful features for severity evaluation or prognosis prediction.…”

“…These studies have shown that deep learning can detect stroke lesions more accurately than traditional machine learning techniques and can extract meaningful features for severity evaluation or prognosis prediction. Some researchers proposed lesion segmentation techniques for acute ischemic stroke patients based on a U-Net architecture 16,17 . To efficiently exploit the contextual information of volumetric MRI data, Zhang et al 18 proposed a stroke lesion segmentation technique using a 3-D fully connected-DenseNet.…”