Exploiting Interslice Correlation for MRI Prostate Image Segmentation, from Recursive Neural Networks Aspect

Abstract

Segmentation of the prostate from Magnetic Resonance Imaging (MRI) plays an important role in prostate cancer diagnosis. However, the lack of clear boundary and significant variation of prostate shapes and appearances make the automatic segmentation very challenging. In the past several years, approaches based on deep learning technology have made significant progress on prostate segmentation. However, those approaches mainly paid attention to features and contexts within each single slice of a 3D volume. As a result, this kind of approaches faces many difficulties when segmenting the base and apex of the prostate due to the limited slice boundary information. To tackle this problem, in this paper, we propose a deep neural network with bidirectional convolutional recurrent layers for MRI prostate image segmentation. In addition to utilizing the intraslice contexts and features, the proposed model also treats prostate slices as a data sequence and utilizes the interslice contexts to assist segmentation. The experimental results show that the proposed approach achieved significant segmentation improvement compared to other reported methods.

Prostate zonal segmentation in 1.5T and 3T T2W MRI using a convolutional neural network

“…2,27 We found lower DSCs for apex and base compared to the mid-gland, which is consistent with all submitted algorithms in the PROMISE12 challenge on WG segmentation, and the zonal segmentation studies by Qiu et al 8,33,34 Due to unclear boundaries and partial volume effects, the segmentation of apex and base is more challenging, also for radiologists. 26,33 Since the apex and base are close to sensitive organs during, for example, radiation therapy these regions are especially important to report for segmentation algorithms. 36 We chose to implement a modified version of the U-net architecture for our zonal segmentation approach because it has proven useful for medical image segmentation even with a limited amount of data.…”

“…39 Another approach to better capture spatial context is the use of recurrent neural networks, where the current input is depended on the previous one. 26 Several hyperparameters can be optimized for CNNs, such as learning rate, optimization function, number of epochs, and batch size. Improvement in network performance might be seen if the parameters are optimized; however, these hyperparameters are believed to be of secondary importance, compared to network architecture and preprocessing.…”

“…25 Zhu et al, for example, did WG prostate segmentation using U-net on T2W images obtained by a 3T scanner from 80 patients and obtained a mean DSC of 0.865. 26 CNNs have shown promising results within medical image segmentation but have not been investigated for zonal segmentation. In this study, we proposed a zonal segmentation algorithm using T2W MRI on prostate examinations from two different scanners [GE 1.5 (with ERC) and Siemens 3T] using a modified U-net architecture.…”

Automatic Segmentation of Prostate Magnetic Resonance Imaging Using Generative Adversarial Networks

“…Among these, Zhu et al [19] developed a deeply supervised two-dimensional (2D) U-Net model, and Milletari et al [14] introduced V-Net as a 3D prostate image segmentation model. Further, Zhu et al [20] proposed a recursive neural networks (RNNs) UR-Net model, they treat prostate image slices as data sequences and use intra-slice features to improve the performance of prostate segmentation. Cheng et al [21] presented an active appearance model (AAM) based on atlas and a deep learning model, and demonstrated higher precision for prostate segmentation on MRI.…”

Characterization of Complex Image Spatial Structures Based on Symmetrical Weibull Distribution Model for Texture Pattern Classification

“…Texture is an important visual perception cue ubiquitously existing in almost all-natural images. TP classification (TPC) plays an essential role in a variety of image analysis and computer vision (CV) tasks, such as image segmentation [1], scene classification, object recognition and image retrieval [2], and many others more which cannot be enumerated exhaustively [3]. Developing a TPC or identification system mainly involves two steps, designing effective algorithm for TP feature extraction [2] and training a classifier for TPC [4,5].…”