Dataset Information

Asymmetrical Multi-task Attention U-Net for the Segmentation of Prostate Bed in CT Image.

ABSTRACT: Segmentation of the prostate bed, the residual tissue after the removal of the prostate gland, is an essential prerequisite for post-prostatectomy radiotherapy but also a challenging task due to its non-contrast boundaries and highly variable shapes relying on neighboring organs. In this work, we propose a novel deep learning-based method to automatically segment this "invisible target". As the main idea of our design, we expect to get reference from the surrounding normal structures (bladder&rectum) and take advantage of this information to facilitate the prostate bed segmentation. To achieve this goal, we first use a U-Net as the backbone network to perform the bladder&rectum segmentation, which serves as a low-level task that can provide references to the high-level task of the prostate bed segmentation. Based on the backbone network, we build a novel attention network with a series of cascaded attention modules to further extract discriminative features for the high-level prostate bed segmentation task. Since the attention network has one-sided dependency on the backbone network, simulating the clinical workflow to use normal structures to guide the segmentation of radiotherapy target, we name the final composition model asymmetrical multi-task attention U-Net. Extensive experiments on a clinical dataset consisting of 186 CT images demonstrate the effectiveness of this new design and the superior performance of the model in comparison to the conventional atlas-based methods for prostate bed segmentation. The source code is publicly available at https://github.com/superxuang/amta-net.

SUBMITTER: Xu X

PROVIDER: S-EPMC8221064 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Asymmetrical Multi-task Attention U-Net for the Segmentation of Prostate Bed in CT Image.

Xu Xuanang X Lian Chunfeng C Wang Shuai S Wang Andrew A Royce Trevor T Chen Ronald R Lian Jun J Shen Dinggang D

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention 20200929

Segmentation of the prostate bed, the residual tissue after the removal of the prostate gland, is an essential prerequisite for post-prostatectomy radiotherapy but also a challenging task due to its non-contrast boundaries and highly variable shapes relying on neighboring organs. In this work, we propose a novel deep learning-based method to automatically segment this "invisible target". As the main idea of our design, we expect to get reference from the surrounding normal structures (bladder&re ...[more]

PMID: 34179897

Similar Datasets

Project description:BackgroundThe precise identification of the position and form of a tumor mass can improve early diagnosis and treatment. However, due to the complicated tumor categories and varying sizes and forms, the segregation of brain gliomas and their internal sub-regions is still very challenging. This study sought to design a new deep-learning network based on three-dimensional (3D) U-Net to address its shortcomings in brain tumor segmentation (BraTS) tasks.MethodsWe developed a 3D dilated multi-scale residual attention U-Net (DMRA-U-Net) model for magnetic resonance imaging (MRI) BraTS. It used dilated convolution residual (DCR) modules to better process shallow features, multi-scale convolution residual (MCR) modules in the bottom encoding path to create richer and more comprehensive feature expression while reducing overall information loss or blurring, and a channel attention (CA) module between the encoding and decoding paths to address the problem of retrieving and preserving important features during the processing of deep feature maps.ResultsThe BraTS 2018-2021 datasets served as the training and evaluation datasets for this study. Further, the proposed architecture was assessed using metrics such as the dice similarity coefficient (DSC), Hausdorff distance (HD), and sensitivity (Sens). The DMRA U-Net model segments the whole tumor (WT), and the tumor core (TC), and the enhancing tumor (ET) regions of brain tumors. Using the suggested architecture, the DSCs were 0.9012, 0.8867, and 0.8813, the HDs were 28.86, 13.34, and 10.88 mm, and the Sens was 0.9429, 0.9452, and 0.9303 for the WT, TC, and ET regions, respectively. Compared to the traditional 3D U-Net, the DSC of the DMRA U-Net increased by 4.5%, 2.5%, and 0.8%, the HD of the DMRA U-Net decreased by 21.83, 16.42, and 10.00, the Sens of the DMRA U-Net increased by 0.4%, 0.7%, and 1.4% for the WT, TC, and ET regions, respectively. Further, the results of the statistical comparison of the performance indicators revealed that our model performed well generally in the segmentation of the WT, TC, and ET regions.ConclusionsWe developed a promising tumor segmentation model. Our solution is open sourced and is available at: https://github.com/Gold3nk/dmra-unet.

Project description:PurposeProstate cancer (PCa) is the second most common cancer in males worldwide, requiring improvements in diagnostic imaging to identify and treat it at an early stage. Bi-parametric magnetic resonance imaging (bpMRI) is recognized as an essential diagnostic technique for PCa, providing shorter acquisition times and cost-effectiveness. Nevertheless, accurate diagnosis using bpMRI images is difficult due to the inconspicuous and diverse characteristics of malignant tumors and the intricate structure of the prostate gland. An automated system is required to assist the medical professionals in accurate and early diagnosis with less effort.MethodThis study recognizes the impact of zonal features on the advancement of the disease. The aim is to improve the diagnostic performance through a novel automated approach of a two-step mechanism using bpMRI images. First, pretraining a convolutional neural network (CNN)-based attention-guided U-Net model for segmenting the region of interest which is carried out in the prostate zone. Secondly, pretraining the same type of Attention U-Net is performed for lesion segmentation.ResultsThe performance of the pretrained models and training an attention-guided U-Net from the scratch for segmenting tumors on the prostate region is analyzed. The proposed attention-guided U-Net model achieved an area under the curve (AUC) of 0.85 and a dice similarity coefficient value of 0.82, outperforming some other pretrained deep learning models.ConclusionOur approach greatly enhances the identification and categorization of clinically significant PCa by including zonal data. Our approach exhibits exceptional performance in the accurate segmentation of bpMRI images compared to current techniques, as evidenced by thorough validation of a diverse dataset. This research not only enhances the field of medical imaging for oncology but also underscores the potential of deep learning models to progress PCa diagnosis and personalized patient care.

Dataset Information

Asymmetrical Multi-task Attention U-Net for the Segmentation of Prostate Bed in CT Image.

Publications

Asymmetrical Multi-task Attention U-Net for the Segmentation of Prostate Bed in CT Image.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets