An Improved Breast Cancer Nuclei Segmentation Method Based on UNet++

Wang, Haonan; Li, Yinhan; Luo, Zhiyi

doi:10.1145/3404555.3404577

Cited by 7 publications

(3 citation statements)

References 20 publications

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“…In [107], a powerful improvement U-Net++ is proposed to segment tiny breast cancer nuclei. The difference between the improved U-Net++ and the baseline U-Net++ [105] is that an Inception-Resnet-V2 network is integrated into U-Net++ and the network improves U-Net++ segmentation capabilities.…”

Section: U-net Redesigned Skip Connectionsmentioning

confidence: 99%

A State-of-the-art Survey of U-Net in Microscopic Image Analysis: from Simple Usage to Structure Mortification

Wu¹,

Liu²,

Li³

et al. 2022

Preprint

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Image analysis technology is used to solve the inadvertences of artificial traditional methods in disease, wastewater treatment, environmental change monitoring analysis and convolutional neural networks (CNN) play an important role in microscopic image analysis. An important step in detection, tracking, monitoring, feature extraction, modeling and analysis is image segmentation, in which U-Net has increasingly applied in microscopic image segmentation. This paper comprehensively reviews the development history of U-Net, and analyzes various research results of various segmentation methods since the emergence of U-Net and conducts a comprehensive review of related papers. First, This paper has summarizes the improved methods of U-Net and then listed the existing significances of image segmentation techniques and their improvements that has introduced over the years. Finally, focusing on the different improvement strategies of U-Net in different papers, the related work of each application target is reviewed according to detailed technical categories to facilitate future research. Researchers can clearly see the dynamics of transmission of technological development and keep up with future trends in this interdisciplinary field.

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Section: U-net Redesigned Skip Connectionsmentioning

confidence: 99%

A State-of-the-art Survey of U-Net in Microscopic Image Analysis: from Simple Usage to Structure Mortification

Wu¹,

Liu²,

Li³

et al. 2022

Preprint

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“…Fig. 3(c) shows that UNet++ uses nested and dense skip connections, and the redesigned skip connections aim to reduce the semantic gap between the feature maps of the encoder and decoder [15]. Both of the above structures are short of exploring image information on a complete scale.…”

Section: Comparison With Unet and Unet++mentioning

confidence: 99%

Claw U-Net: A Unet-based Network with Deep Feature Concatenation for Scleral Blood Vessel Segmentation

Yao¹,

Tang²,

Hu³

et al. 2020

Preprint

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Sturge-Weber syndrome (SWS) is a vascular malformation disease, and it may cause blindness if the patient's condition is severe. Clinical results show that SWS can be divided into two types based on the characteristics of scleral blood vessels. Therefore, how to accurately segment scleral blood vessels has become a significant problem in computer-aided diagnosis. In this research, we propose to continuously upsample the bottom layer's feature maps to preserve image details, and design a novel Claw UNet based on UNet for scleral blood vessel segmentation. Specifically, the residual structure is used to increase the number of network layers in the feature extraction stage to learn deeper features. In the decoding stage, by fusing the features of the encoding, upsampling, and decoding parts, Claw UNet can achieve effective segmentation in the fine-grained regions of scleral blood vessels. To effectively extract small blood vessels, we use the attention mechanism to calculate the attention coefficient of each position in images. Claw UNet outperforms other UNet-based networks on scleral blood vessel image dataset.

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“…The performance was compared to different filters, such as Prewitt, LoG, and Canny, with the tested solutions providing comparable or better performance. Wang et al (2020) [ 26 ] also demonstrated the application of image segmentation on breast cancer nuclei. The researchers applied the U-Net++ architecture, with Inception-ResNet-V2 used as a backbone, allowing for increased performance compared to previous research.…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation of Urinary Bladder Cancer Masses from CT Images: A Transfer Learning Approach

Šegota

Lorencin

Smolić

et al. 2021

Biology

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Urinary bladder cancer is one of the most common cancers of the urinary tract. This cancer is characterized by its high metastatic potential and recurrence rate. Due to the high metastatic potential and recurrence rate, correct and timely diagnosis is crucial for successful treatment and care. With the aim of increasing diagnosis accuracy, artificial intelligence algorithms are introduced to clinical decision making and diagnostics. One of the standard procedures for bladder cancer diagnosis is computer tomography (CT) scanning. In this research, a transfer learning approach to the semantic segmentation of urinary bladder cancer masses from CT images is presented. The initial data set is divided into three sub-sets according to image planes: frontal (4413 images), axial (4993 images), and sagittal (996 images). First, AlexNet is utilized for the design of a plane recognition system, and it achieved high classification and generalization performances with an AUCmicro¯ of 0.9999 and σ(AUCmicro) of 0.0006. Furthermore, by applying the transfer learning approach, significant improvements in both semantic segmentation and generalization performances were achieved. For the case of the frontal plane, the highest performances were achieved if pre-trained ResNet101 architecture was used as a backbone for U-net with DSC¯ up to 0.9587 and σ(DSC) of 0.0059. When U-net was used for the semantic segmentation of urinary bladder cancer masses from images in the axial plane, the best results were achieved if pre-trained ResNet50 was used as a backbone, with a DSC¯ up to 0.9372 and σ(DSC) of 0.0147. Finally, in the case of images in the sagittal plane, the highest results were achieved with VGG-16 as a backbone. In this case, DSC¯ values up to 0.9660 with a σ(DSC) of 0.0486 were achieved. From the listed results, the proposed semantic segmentation system worked with high performance both from the semantic segmentation and generalization standpoints. The presented results indicate that there is the possibility for the utilization of the semantic segmentation system in clinical practice.

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An Improved Breast Cancer Nuclei Segmentation Method Based on UNet++

Cited by 7 publications

References 20 publications

A State-of-the-art Survey of U-Net in Microscopic Image Analysis: from Simple Usage to Structure Mortification

A State-of-the-art Survey of U-Net in Microscopic Image Analysis: from Simple Usage to Structure Mortification

Claw U-Net: A Unet-based Network with Deep Feature Concatenation for Scleral Blood Vessel Segmentation

Semantic Segmentation of Urinary Bladder Cancer Masses from CT Images: A Transfer Learning Approach

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