An Improved 2D U-Net Model Integrated Squeeze-and-Excitation Layer for Prostate Cancer Segmentation

Abstract

In this paper, we proposed an improved 2D U-Net model integrated squeeze-and-excitation layer for prostate cancer segmentation. The proposed model combined a more complex 2D U-Net model and squeeze-and-excitation technique. The model consisted of an encoder stage and a decoder stage. The encoder stage aims to extract features of the input, which contains CONV blocks, SE layers, and max-pooling layers for improving the feature extraction capability of the model. The decoder aims to map the extracted features to the original image with CONV blocks, SE layers, and upsampling layers. The SE layer is implemented to learn more global and local features. Experiments on the public dataset PROMISE12 have demonstrated that the proposed model could achieve state-of-the-art segmentation performance compared with other traditional methods.

UNet Deep Learning Architecture for Segmentation of Vascular and Non-Vascular Images: A Microscopic Look at UNet Components Buffered With Pruning, Explainable Artificial Intelligence, and Bias

“…To begin with, the encoder is the most adapted and most changeable component of the UNet architecture. Since it is practically not possible to study each of the architectural variations in the encoder, we have therefore listed here the 23 variations (E1 to E23, representing encoder changes) along with their references in a tabular format and it is as follows: (E1) conventional system (Ronneberger) [43][44][45][46][47][48][49][50][51][52]90]; (E2) cascade of convolutions [77,91,99,116,117]; (E3) parallel convolutions (multiple convolution network) [57]; (E4) convolution with dropout [70,76,86,95,101,102,134,138]; (E5) Residual network [76,78,105,129,135,138,[149][150][151]; (E6) Xception encoder [56,88,112]; (E7) encoder layers with independent inputs [104,140]; (E8) squeeze excitation (SE) network [92,103,138]; (E9) pooling types (max pooling, global average pooling) [95]; (E10) input image dimension change with changing filter (channe...…”

“…The different fundamental blocks which were adapted for UNet modification were (Table 4): (1) residual block [75,76,78,84,88,105,129,135,138]; (2) classifier in encoder [145]; (3) Xception block [56,88]; (4) dense layer block [68,100,102,122,142]; (5) recurrent residual block ; (6) attention block [65, 66, 71, 75, 113, 125, 128, 129, 131-135, 139, 161]; (7) dropout layer [70,76,86,95,101,102,134,138]; (8) dilated convolution [67,76]; (9) transpose convolution [66,88,95,137,139]; (10) SE network [92,103,125,133,138], and (11) squeeze and excitation block [92,103,125,133,138].…”

“…Therefore, HF-UNet had the ability to learn the shared representations hierarchically for different tasks and preserve the specificities of learned representations for different tasks simultaneously. Liu et al [92] proposed an improved 2D UNet model that integrated the squeeze-andexcitation (SE) layer for prostate cancer segmentation. The SE layer was used to extract only the important features.…”

“…Architectures used in both vascular and nonvascular UNet paradigms are the different variations of UNet like UNet with dense block [122], parallel fusion, and serial embedding multiscale feature dense UNet [102], UNet with spatial attention [139], the combination of more than one UNet [186], weighted attention UNet [105], pyramid UNet [106] in the case of retinal vascular, 3D UNet for renal vascular [191] and in case of prostate nonvascular UNet variations like UNet with Xception [88], UNet with residual block [84,189] , 2D bridged UNet [89], UNet with SE block [92], UNet with attention gate [132], UNet with more layers (8) [84], UNet with task consistency learning [83], the combination of UNets (ZNet) [85], and cascaded UNet [93,189], 3D UNet for proximal femur [187]; (ii) It is observed that dropout block [92,103,132,139], residual block [84,105,189] (Figure 19), SE block (Figure 20), attention gate block [103,132,186] (Figure 21), inception block [102], and dense block [102,122] are common in both vascular and non-vascular UNet paradigms; (iii) The augmentation process is also common in both vascular and non-vascular paradigms; and 3D UNet (Figure 22).…”

“…For example, the ZNet (Zhang et al [85]) is capable of capturing more features in a multi-level fashion by using concatenation and dense connection. Attention-based UNet [83,92] was suitable when image contains large background noise as the attention gate is capable of extracting relevant parts and ignoring irrelevant ones. When we need segmentation of the prostate gland and peripheral zone in one pass, then we can use cascaded MRes-UNet [189], as the first MRes-UNet predicts the mask for the prostate gland.…”