Bladder Cancer Multi-Class Segmentation in MRI With Pyramid-In-Pyramid Network

Liu, Jingxin; Liu, Libo; Xu, Bolei; Hou, Xianxu; Liu, Bozhi; Chen, Xin; Shen, Linlin; Qiu, Guoping

doi:10.1109/isbi.2019.8759422

Cited by 20 publications

(29 citation statements)

References 11 publications

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“…Xiangchun Li et al used ultrasound images to train a convolutional neural network to diagnose thyroid cancer, and the area under the curve value of the model on the validation set reached 0.912 7 . In the field of bladder tumours, Jingxin Liu used deep learning technology to detect bladder tumours in magnetic resonance imaging (MRI) images 8 . Kenny et al used deep learning in computed tomography (CT) images to assess the treatment response for bladder cancer 9 .…”

Section: Introductionmentioning

confidence: 99%

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Automatic recognition of bladder tumours using deep learning technology and its clinical application

Yang

Wang

et al. 2020

Robotics Computer Surgery

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Background Bladder cancer is a kind of tumors with a high recurrence rate. The improvement of the cure rate and prognosis of bladder tumor depends on the accurate recognition of bladder tumor under the cystoscope. Aims To verify that deep learning technology can identify bladder cancer images. Materials and Methods In this study, 1200 cystoscopic cancer images from 224 patients with bladder cancer and 1150 cystoscopic images from 221 patients with no bladder cancer were collected. Three convolutional neural networks (LeNet, AlexNet and GoogLeNet), and the EasyDL deep learning platform were used to train deep learning models to distinguish images of bladder cancer. The diagnostic efficiency of deep learning model and urology experts was compared. Results The efficiency of EasyDL was the highest, and the accuracy was 96.9%. The efficiency of GoogLeNet was the second highest, and the accuracy was 92.54%. Among the 33 bladder cancer nodes and 11 no bladder cancer nodes, the accuracy of the neural network was 83.36% and that of medical experts was 84.09% (p > 0.05). Discussion This study used convolutional neural networks to recognize bladder tumor in the clinical. Although these three networks (LeNet, AlexNet and GoogLeNet) had a relatively basic network architecture, they achieved good results in the classification task of cystoscopic images. The deep learning system had a recognition efficiency no less than that of experienced clinical experts. Conclusion This study proved the validity of the convolutional neural network for bladder tumor diagnosis based on the cystoscope.

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Section: Introductionmentioning

confidence: 99%

“…7 In the field of bladder tumours, Jingxin Liu used deep learning technology to detect bladder tumours in magnetic resonance imaging (MRI) images. 8 Kenny et al used deep learning in computed tomography (CT) images to assess the treatment response for bladder cancer. 9 In summary, most current studies are based on CT or MRI images of bladder tumours.…”

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confidence: 99%

Automatic recognition of bladder tumours using deep learning technology and its clinical application

Yang

Wang

et al. 2020

Robotics Computer Surgery

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“…As tumor regions are unpredictable and they can be either very small or very large regions this method benefits to both cases. Following this work, similar approaches have been investigated under different conditions [76,77,80]. Gsaxner et al [76] compared to well-known architectures, i.e., FCN [38] and ResNet [89], to segment the bladder in CT scans.…”

Section: Fully-connected Architecturesmentioning

confidence: 99%

“…Thereby, incorporating shape prior will likely hamper the performance of the deep model. Similarly to [27], Liu et al [80] proposed architectural modifications on UNet to accommodate large shape variations on the target in a multi-class segmentation scenario. In addition of dilated convolutions at multiple levels, they also incorporated multi-scale predictions.…”

Section: Fully-connected Architecturesmentioning

confidence: 99%

“…An important issue with these practices is that the generability of the approaches is not fully demonstrated, as the testing set is relatively small. This is further aggravated in several approaches [74,75,76,80], which do not employ an independent validation set to fix the hyper-parameters or as an early stopping criteria. Quantitative results.…”

Section: Evaluation On the Literaturementioning

confidence: 99%

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Bladder segmentation based on deep learning approaches: current limitations and lessons

Bandyk,

Gopireddy,

Lall

et al. 2021

Preprint

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Precise determination and assessment of bladder cancer (BC) extent of muscle invasion involvement guides proper risk stratification and personalized therapy selection. In this context, segmentation of both bladder walls and cancer are of pivotal importance, as it provides invaluable information to stage the primary tumor. Hence, multiregion segmentation on patients presenting with symptoms of bladder tumors using deep learning heralds a new level of staging accuracy and prediction of the biologic behavior of the tumor. Nevertheless, despite the success of these models in other medical problems, progress in multiregion bladder segmentation is still at a nascent stage, with just a handful of works tackling a multiregion scenario. Furthermore, most existing approaches systematically follow prior literature in other clinical problems, without casting a doubt on the validity of these methods on bladder segmentation, which may present different challenges. Inspired by this, we provide an in-depth look at bladder cancer segmentation using deep learning models. The critical determinants for accurate differentiation of muscle invasive disease, current status of deep learning based bladder segmentation, lessons and limitations of prior work are highlighted.

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Cascade Path Augmentation Unet for bladder cancer segmentation in MRI

Cai

Chen

et al. 2022

Medical Physics

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Background Treatment choices for patients with bladder cancer (BCa) are determined by the presence of muscular invasion. The precise segmentation of the inner and outer walls (IW and OW), as well as the bladder tumor (BT), is crucial for improving computer‐aided diagnosis of muscle‐invasive bladder cancer (MIBC). Purpose To propose a novel deep learning‐based model to improve the segmentation accuracy of the IW, OW, and BT, which can be useful in clinical practice. Methods We proposed a Cascade Path Augmentation Unet (CPA‐Unet) network to conduct multi‐regional segmentation of the bladder using 1545 T2‐weighted MRI scans. The model employs a cascade strategy to eliminate the redundant information in the background. Unet is used to segment the bladder from the background in the rough segmentation. The path augmentation structure is used in the fine segmentation to mine multi‐scale features. Additionally, the partial dense connection is adopted as the skip connection module to concatenate the low‐ and high‐level sematic features. Results The CPA‐Unet is trained using 1391 T2WI slices and tested using 154 T2WI slices. In comparison to previous deep learning‐based methods, the CPA‐Unet achieves superior segmentation results in terms of Dice similarity coefficient (DSC) and Hausdorff distance (HD) (IW: DSC = 98.19%, HD = 2.07 mm; OW: DSC = 82.24%, HD = 2.62 mm; BT: DSC = 87.40%, HD = 0.76 mm). Conclusions Our proposed CPA‐Unet network is capable of segmenting the bladder into its IW and OW, as well as tumors. The segmentation results provide a reliable and effective foundation for computer‐assisted clinical diagnosis of MIBC.

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Bladder Cancer Multi-Class Segmentation in MRI With Pyramid-In-Pyramid Network

Cited by 20 publications

References 11 publications

Automatic recognition of bladder tumours using deep learning technology and its clinical application

Automatic recognition of bladder tumours using deep learning technology and its clinical application

Bladder segmentation based on deep learning approaches: current limitations and lessons

Cascade Path Augmentation Unet for bladder cancer segmentation in MRI

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