Deeply self-supervised contour embedded neural network applied to liver segmentation

Chung, Minyoung; Lee, Jingyu; Lee, Minkyung; Lee, Jeongjin; Shin, Yeong-Gil

doi:10.1016/j.cmpb.2020.105447

Cited by 31 publications

(14 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Different from other existing methods, our method has two important characteristics regarding the proposed MFU-net. First, the previous liver tumor segmentation was a two-way process or cascaded approach (18,(29)(30)(31)(32)(33). In other words, tumor segmentation has been done after liver segmentation from the abdominal CT scan image.…”

Section: Discussionmentioning

confidence: 99%

Effects of Multiple Filters on Liver Tumor Segmentation From CT Images

Yang

Lee

et al. 2021

Front. Oncol.

View full text Add to dashboard Cite

Segmentation of liver tumors from Computerized Tomography (CT) images remains a challenge due to the natural variation in tumor shape and structure as well as the noise in CT images. A key assumption is that the performance of liver tumor segmentation depends on the characteristics of multiple features extracted from multiple filters. In this paper, we design an enhanced approach based on a two-class (liver, tumor) convolutional neural network that discriminates tumor as well as liver from CT images. First, the contrast and intensity values in CT images are adjusted and high frequencies are removed using Hounsfield units (HU) filtering and standardization. Then, the liver tumor is segmented from entire images with multiple filter U-net (MFU-net). Finally, a quantitative analysis is carried out to evaluate the segmentation results using three different methods: boundary-distance-based metrics, size-based metrics, and overlap-based metrics. The proposed method is validated on CT images from the 3Dircadb and LiTS dataset. The results demonstrate that the multiple filters are useful for extracting local and global feature simultaneously, minimizing the boundary distance errors, and our approach demonstrates better performance in heterogeneous tumor regions of CT images.

show abstract

Section: Discussionmentioning

confidence: 99%

Effects of Multiple Filters on Liver Tumor Segmentation From CT Images

Yang

Lee

et al. 2021

Front. Oncol.

View full text Add to dashboard Cite

show abstract

“…To alleviate overfitting and to achieve good generalizations, we applied three different augmentation methods to the training set: the random crop method, the random scaled method, and cutout augmentation [ 11 12 ] with 80% probability. We applied a random-position zero mask to images with sizes ranging from L/5 to L/4, where L is the length of the image.…”

Section: Methodsmentioning

confidence: 99%

Expert-level segmentation using deep learning for volumetry of polycystic kidney and liver

Shin

Kim

Lee³

et al. 2020

Investig Clin Urol

View full text Add to dashboard Cite

Purpose Volumetry is used in polycystic kidney and liver diseases (PKLDs), including autosomal dominant polycystic kidney disease (ADPKD), to assess disease progression and drug efficiency. However, since no rapid and accurate method for volumetry has been developed, volumetry has not yet been established in clinical practice, hindering the development of therapies for PKLD. This study presents an artificial intelligence (AI)-based volumetry method for PKLD. Materials and Methods The performance of AI was first evaluated in comparison with ground-truth (GT). We trained a V-net-based convolutional neural network on 175 ADPKD computed tomography (CT) segmentations, which served as the GT and were agreed upon by 3 experts using images from 214 patients analyzed with volumetry. The dice similarity coefficient (DSC), interobserver correlation coefficient (ICC), and Bland–Altman plots of 39 GT and AI segmentations in the validation set were compared. Next, the performance of AI on the segmentation of 50 random CT images was compared with that of 11 PKLD specialists based on the resulting DSC and ICC. Results The DSC and ICC of the AI were 0.961 and 0.999729, respectively. The error rate was within 3% for approximately 95% of the CT scans (error<1%, 46.2%; 1%≤error<3%, 48.7%). Compared with the specialists, AI showed moderate performance. Furthermore, an outlier in our results confirmed that even PKLD specialists can make mistakes in volumetry. Conclusions PKLD volumetry using AI was fast and accurate. AI performed comparably to human specialists, suggesting its use may be practical in clinical settings.

show abstract

“…The layer-wise segmentation CNN consists of deep downsampling of feature maps, which may cause the gradient to vanish at low resolution levels, which affects training and network performance. To overcome these problems, deeply supervised methods [28][29][30][31] could be applied, which increase the discriminative capability and prevent the gradient from vanishing for a deep segmentation network. Because of these advantages, we applied deeply supervised learning to the segmentation network.…”

Section: Deeply Supervised Dilated Fc-densenet (D2fc-dn)mentioning

confidence: 99%

“…Mo et al [29] proposed a deep-supervised FCN for segmentation of the vessel from retinal images. Chung et al [30] proposed a dense block applied FCN with deep supervision for segmentation of the liver from CT images. Lei et al [31] proposed a method for ultrasound prostate segmentation based on 3D V-Net with a deep supervision mechanism; this method demonstrated high accuracy, with a DSC of 0.92.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of finger tendon and synovial sheath in ultrasound image using deep convolutional neural network

Kuok¹,

Yang

Tsai³

et al. 2020

BioMed Eng OnLine

View full text Add to dashboard Cite

Background: Trigger finger is a common hand disease, which is caused by a mismatch in diameter between the tendon and the pulley. Ultrasound images are typically used to diagnose this disease, which are also used to guide surgical treatment. However, background noise and unclear tissue boundaries in the images increase the difficulty of the process. To overcome these problems, a computer-aided tool for the identification of finger tissue is needed. Results: Two datasets were used for evaluation: one comprised different cases of individual images and another consisting of eight groups of continuous images. Regarding result similarity and contour smoothness, our proposed deeply supervised dilated fully convolutional DenseNet (D2FC-DN) is better than ATASM (the state-of-art segmentation method) and representative CNN methods. As a practical application, our proposed method can be used to build a tendon and synovial sheath model that can be used in a training system for ultrasound-guided trigger finger surgery. Conclusion: We proposed a D2FC-DN for finger tendon and synovial sheath segmentation in ultrasound images. The segmentation results were remarkably accurate for two datasets. It can be applied to assist the diagnosis of trigger finger by highlighting the tissues and generate models for surgical training systems in the future. Methods: We propose a novel finger tendon segmentation method for use with ultrasound images that can also be used for synovial sheath segmentation that yields a more complete description for analysis. In this study, a hybrid of effective convolutional neural network techniques are applied, resulting in a deeply supervised dilated fully convolutional DenseNet (D2FC-DN), which displayed excellent segmentation performance on the tendon and synovial sheath.

show abstract

Deeply self-supervised contour embedded neural network applied to liver segmentation

Cited by 31 publications

References 34 publications

Effects of Multiple Filters on Liver Tumor Segmentation From CT Images

Effects of Multiple Filters on Liver Tumor Segmentation From CT Images

Expert-level segmentation using deep learning for volumetry of polycystic kidney and liver

Segmentation of finger tendon and synovial sheath in ultrasound image using deep convolutional neural network

Contact Info

Product

Resources

About