Deep-learning method for tumor segmentation in breast DCE-MRI

Zhang, Lei; Luo, Zhimeng; Chai, Ruimei; Arefan, Dooman; Sumkin, Jules H.; Wu, Shandong

doi:10.1117/12.2513090

Cited by 26 publications

(23 citation statements)

References 13 publications

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“…Peng et al [13] proposed an end-to-end cascaded deep ResUNet network to segment the liver lesion, which could increase the prediction results of accuracy and sensitivity. Zhang et al [14] employed U-Net model to segment breast tumors in Dynamic Contrast-Enhanced MRI of 2D and 3D images. Yang et al [15] proposed a multi-task DCNN technique to segment and classify skin lesions.…”

Section: Introductionmentioning

confidence: 99%

An Automatic Bleeding-Rank System for Transurethral Resection of the Prostate Surgery Videos Using Machine Learning

Chen

Lin

et al. 2021

Diagnostics

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Benign prostatic hyperplasia (BPH) is the main cause of lower urinary tract symptoms (LUTS) in aging males. Transurethral resection of the prostate (TURP) surgery is performed by a cystoscope passing through the urethra and scraping off the prostrate piece by piece through a cutting loop. Although TURP is a minimally invasive procedure, bleeding is still the most common complication. Therefore, the evaluation, monitoring, and prevention of interop bleeding during TURP are very important issues. The main idea of this study is to rank bleeding levels during TURP surgery from videos. Generally, to judge bleeding level by human eyes from surgery videos is a difficult task, which requires sufficient experienced urologists. In this study, machine learning-based ranking algorithms are proposed to efficiently evaluate the ranking of blood levels. Based on the visual clarity of the surgical field, the four ranking of blood levels, including score 0: excellent; score 1: acceptable; score 2: slightly bad; and 3: bad, were identified by urologists who have sufficient experience in TURP surgery. The results of extensive experiments show that the revised accuracy can achieve 90, 89, 90, and 91%, respectively. Particularly, the results reveal that the proposed methods were capable of classifying the ranking of bleeding level accurately and efficiently reducing the burden of urologists.

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Section: Introductionmentioning

confidence: 99%

An Automatic Bleeding-Rank System for Transurethral Resection of the Prostate Surgery Videos Using Machine Learning

Chen

Lin

et al. 2021

Diagnostics

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“…For the problem of 2D/3D slice or ROI selection, Zhang et al [10], [11] used a 2D-based slice-by-slice method for whole breast segmentation by a convolutional network on DCE-MRI and DWI sequences and used both 2D and 3D DCE-MRI data for breast tumor segmentation.…”

Section: Introductionmentioning

confidence: 99%

A Clifford Analytic Signal-Based Breast Lesion Segmentation Method for 4D Spatial-Temporal DCE-MRI Sequences

et al. 2020

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Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) has been increasingly used for lesion detection in breast cancer diagnosis for its capability to provide spatial-temporal information. However, the massive and complex 4D spatial-temporal DCE-MRI data make the diagnosis process lengthy and error-prone. Moreover, normal fibroglandular tissue is occasionally enhanced through background parenchymal enhancement (BPE), which can degrade the performance of current algorithms. We propose a new method using a 3D Clifford analytic signal (CAS) approach for breast lesion segmentation of DCE-MRI data. A 2D temporal image is constructed from all the 2D DCE-MRI slices at different scanning time points on a given transverse plane, according to the CAS approach. Then, a 3D Clifford temporal image (CTI) is constructed by successively stacking temporal images. The proposed CTI can distinguish lesion regions both visually and quantitatively compared to the traditional DCE-MRI subtraction image. Finally, we employ a fully convolutional network (FCN) model for breast lesion segmentation using the CTI as one of the inputs. Experimental results on an independent public dataset (TCIA QIN breast DCE-MRI) and a private household breast DCE-MRI dataset (TBD) show that the proposed method can achieve superior performance over current methods, both qualitatively and quantitatively. INDEX TERMS Breast DCE-MRI, breast lesion segmentation, fully convolutional network, clifford analytic signal, clifford temporal image.

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“…In recent years, due to the active studies and achievements regarding artificial intelligence (AI), AI technologies based on deep learning [1] have been successfully applied in speech recognition [2], natural language processing [3] and computer vision. The deep convolutional neural networks (CNNs) have outperformed state‐of‐the‐art algorithms in many visual recognition tasks, such as image classification [4], image retrieval [5], object detection [6], semantic segmentation [7], and so on. The classical CNNs evolve from VGG [8], Inception [9–11], residual network (ResNet) [12] to DenseNet [13].…”

Section: Introductionmentioning

confidence: 99%

Residual‐wider convolutional neural network for image recognition

Chen

2020

IET image process

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Recent works show that the performance of convolutional neural networks (CNNs) could be improved by making the network wider and introducing residual connections. For example, the Inception architecture is one of the classical models. However, the structure of the series of Inception is complex, and there are more convolution layers and existing redundant image feature information. In order to further improve the performance and observability of CNNs, a novel type of network structure based on two modules, wider module and residual module, is proposed to make full advantage of the information of the image and learn more abundant features in this study, which is called residual-wider network (R-WN). The structure of R-WN is easy to understand and adopts modular design method. Experiential results demonstrate that the proposed R-WN with optimal structure and much fewer convolution layers could achieve much better image recognition results on four classical open data sets (MNIST, CIFAR10, SVHN, and Oxford Flowers 17).

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Deep-learning method for tumor segmentation in breast DCE-MRI

Cited by 26 publications

References 13 publications

An Automatic Bleeding-Rank System for Transurethral Resection of the Prostate Surgery Videos Using Machine Learning

An Automatic Bleeding-Rank System for Transurethral Resection of the Prostate Surgery Videos Using Machine Learning

A Clifford Analytic Signal-Based Breast Lesion Segmentation Method for 4D Spatial-Temporal DCE-MRI Sequences

Residual‐wider convolutional neural network for image recognition

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