ProSegNet: A New Network of Prostate Segmentation Based on MR Images

Abstract

Prostate cancer is the most common cancer in men after lung cancer. Generally, the segmentation of the prostate is the preprocessing work for the diagnosis of prostate cancer. Aiming at the variety of prostate and the similarity of visual characteristics between prostates and their surroundings, this paper proposes a new prostate segmentation network based on MR images, denoted as ProSegNet. ProSegNet consists of two parts: encoder and decoder. To improve the feature extraction capability of the encoder, we use dense blocks as the feature extraction unit, and at the same time introduce a cross-stage partial (CSP) structure to reduce the amount of calculation. In the design of the decoder structure, we integrated the spatial attention mechanism and the channel attention mechanism to enable it to focus on the important features while ignoring the invalid features. In addition, to segment the prostate more accurately, we add a prostate contour segmentation branch to the output of the segmentation network to learn the contour features of the prostate. Finally, to alleviate the problem of small intensity difference between the prostate and surrounding tissues, we designed a truncated intensity stretching image enhancement method. The performance of ProSegNet has been experimentally verified in the Promise12 and ProstateX datasets. On the Promise12 dataset, the dice similarity coefficient (DSC) and hausdorff (Haus) distance are 0.908 and 9.87 respectively. On the ProstateX dataset, the DSC and Haus reach the results of 0.892 and 10.45, respectively. Experimental results show that the ProSegNet can obtain a competitive performance.INDEX TERMS prostate segmentation, magnetic resonance image, convolutional neural network, attention mechanism, multi-task learning.

A systematic review of deep learning methods for the classification and segmentation of prostate cancer on magnetic resonance images

“…Qian et al 54 introduced an innovative segmentation network referred to as ProSegNet, which is devised for the purpose of prostate segmentation using MR images. Utilizing dense blocks for feature extraction in the encoder and incorporating spatial and channel attention mechanisms in the decoder, ProSegNet achieved robust segmentation performance.…”

“…Utilizing datasets of varying sizes and characteristics, including both public datasets and private datasets, these papers emphasize achieving high diagnostic accuracy in PCa detection tasks. Further, papers 34–64 focus on segmentation accuracy, evaluating methods using metrics like DSC, Hausdorff Distance (HD), or Intersection over Union (IoU). Additionally, they compare segmentation results with existing methods, highlighting improvements in accuracy, efficiency, or robustness, while emphasizing clinical implications for treatment planning and patient outcomes.…”

Improved Prostate Biparameter Magnetic Resonance Image Segmentation Based on Def-UNet

“…Qian et al designed a new method of prostate cancer detection based on MR images, which is recorded as ProCDet, experimental results show that the ProCDet can obtain competitive detection performance [45]. Qian et al proposed a new prostate segmentation network based on MR images, denoted as ProSegNet, which integrated the spatial attention mechanism and the channel attention mechanism to focus on the important features while ignoring the invalid features [46]. Inspired by the above work, we explore a U-Net with deformable operations, namely, Def-UNet, as a neural network structure that can effectively handle the large anatomical variability of the prostate through a deformable convolution block.…”