A two-stage approach for automatic liver segmentation with Faster R-CNN and DeepLab

Tang, Wei; Zou, Dongsheng; Yang, Su; Jing, Shi; Dan, Jingpei; Song, Guowu

doi:10.1007/s00521-019-04700-0

Cited by 60 publications

(30 citation statements)

References 26 publications

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“…The main SegNet applications regard segmentation tasks such as semantic segmentation of prostate cancer [18], gland segmentation from colon cancer histology images [19] and brain tumor segmentation from multi-modal magnetic resonance images [20]. DeepLab v3+ has been used for the semantic segmentation of colorectal polyps [21] and the automatic liver segmentation [22,23].…”

Section: Semantic Segmentation Frameworkmentioning

confidence: 99%

Semantic Segmentation Framework for Glomeruli Detection and Classification in Kidney Histological Sections

et al. 2020

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The evaluation of kidney biopsies performed by expert pathologists is a crucial process for assessing if a kidney is eligible for transplantation. In this evaluation process, an important step consists of the quantification of global glomerulosclerosis, which is the ratio between sclerotic glomeruli and the overall number of glomeruli. Since there is a shortage of organs available for transplantation, a quick and accurate assessment of global glomerulosclerosis is essential for retaining the largest number of eligible kidneys. In the present paper, the authors introduce a Computer-Aided Diagnosis (CAD) system to assess global glomerulosclerosis. The proposed tool is based on Convolutional Neural Networks (CNNs). In particular, the authors considered approaches based on Semantic Segmentation networks, such as SegNet and DeepLab v3+. The dataset has been provided by the Department of Emergency and Organ Transplantations (DETO) of Bari University Hospital, and it is composed of 26 kidney biopsies coming from 19 donors. The dataset contains 2344 non-sclerotic glomeruli and 428 sclerotic glomeruli. The proposed model consents to achieve promising results in the task of automatically detecting and classifying glomeruli, thus easing the burden of pathologists. We get high performance both at pixel-level, achieving mean F-score higher than 0.81, and Weighted Intersection over Union (IoU) higher than 0.97 for both SegNet and Deeplab v3+ approaches, and at object detection level, achieving 0.924 as best F-score for non-sclerotic glomeruli and 0.730 as best F-score for sclerotic glomeruli.

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Section: Semantic Segmentation Frameworkmentioning

confidence: 99%

Semantic Segmentation Framework for Glomeruli Detection and Classification in Kidney Histological Sections

et al. 2020

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“…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.

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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.

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“…For the identification of the candidate TLS regions, a modified pre-trained DeepLab v3+ model [ 29 ] with Inception-ResNet-v2 as the main feature extractor, which employs dropout to avoid overfitting, was utilized. The DeepLab models have been extensively used in the task of semantic medical image segmentation and tested on large volumes of image datasets [ 30 – 34 ]. These models provide a capability in learning multi-scale contextual features through Atrous Spatial Pyramid Pooling (ASPP) and use a decoder module for the refinement of the segmentation results, especially along object boundaries.…”

Section: Methodsmentioning

confidence: 99%

Tertiary lymphoid structures (TLS) identification and density assessment on H&E-stained digital slides of lung cancer

et al. 2021

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Tertiary lymphoid structures (TLS) are ectopic aggregates of lymphoid cells in inflamed, infected, or tumoral tissues that are easily recognized on an H&E histology slide as discrete entities, distinct from lymphocytes. TLS are associated with improved cancer prognosis but there is no standardised method available to quantify their presence. Previous studies have used immunohistochemistry to determine the presence of specific cells as a marker of the TLS. This has now been proven to be an underestimate of the true number of TLS. Thus, we propose a methodology for the automated identification and quantification of TLS, based on H&E slides. We subsequently determined the mathematical criteria defining a TLS. TLS regions were identified through a deep convolutional neural network and segmentation of lymphocytes was performed through an ellipsoidal model. This methodology had a 92.87% specificity at 95% sensitivity, 88.79% specificity at 98% sensitivity and 84.32% specificity at 99% sensitivity level based on 144 TLS annotated H&E slides implying that the automated approach was able to reproduce the histopathologists’ assessment with great accuracy. We showed that the minimum number of lymphocytes within TLS is 45 and the minimum TLS area is 6,245μm2. Furthermore, we have shown that the density of the lymphocytes is more than 3 times those outside of the TLS. The mean density and standard deviation of lymphocytes within a TLS area are 0.0128/μm2 and 0.0026/μm2 respectively compared to 0.004/μm2 and 0.001/μm2 in non-TLS regions. The proposed methodology shows great potential for automated identification and quantification of the TLS density on digital H&E slides.

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A two-stage approach for automatic liver segmentation with Faster R-CNN and DeepLab

Cited by 60 publications

References 26 publications

Semantic Segmentation Framework for Glomeruli Detection and Classification in Kidney Histological Sections

Semantic Segmentation Framework for Glomeruli Detection and Classification in Kidney Histological Sections

Effects of Multiple Filters on Liver Tumor Segmentation From CT Images

Tertiary lymphoid structures (TLS) identification and density assessment on H&E-stained digital slides of lung cancer

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