A Joint Deep Learning Approach for Automated Liver and Tumor Segmentation

Gruber, Nadja; Antholzer, Stephan; Jaschke, Werner; Kremser, Christian; Haltmeier, Markus

doi:10.1109/sampta45681.2019.9030909

Cited by 37 publications

(26 citation statements)

References 8 publications

(10 reference statements)

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“…In particular, the SED model has an excellent recognition rate for the multi-sided irregular shape in Case 3 compared to U-Net where the TP portion is oversized, and to ResNet where the TP is undersized. [29], (e) C-UNet [27], and (f) The proposed SED. Figures 7 and 8 show the comparison of IOU value and accuracy for the four cases of U-Net, ResNet, C-UNet, and our proposed SED.…”

Section: Tumor Segmentatiuon Resultsmentioning

confidence: 99%

“…Image segmentation is the process of dividing a digital image into multiple segments. It is a classic problem in image processing and computer vision and is widely used in medical imaging research [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ]. In the early years, many algorithms were proposed to fulfill image segmentation.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, CNN would be able to an appropriate solution to overcome the above-mentioned problems of tumor variability and feature design issues. Thus, the technologies and applications of CNN performing on organ/lesion segmentation would be of great interest to the medical imaging community [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ], as this approach would have achieved superior performance in medical imaging challenges [ 30 , 31 , 32 , 33 , 34 , 35 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

“…ResNet [29], (e) C-UNet [27], and (f) The proposed SED. Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board (or Ethics Committee) of…”

mentioning

confidence: 99%

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In-Series U-Net Network to 3D Tumor Image Reconstruction for Liver Hepatocellular Carcinoma Recognition

Chen

Liu

et al. 2020

Diagnostics

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Cancer is one of the common diseases. Quantitative biomarkers extracted from standard-of-care computed tomography (CT) scan can create a robust clinical decision tool for the diagnosis of hepatocellular carcinoma (HCC). According to the current clinical methods, the situation usually accounts for high expenditure of time and resources. To improve the current clinical diagnosis and therapeutic procedure, this paper proposes a deep learning-based approach, called Successive Encoder-Decoder (SED), to assist in the automatic interpretation of liver lesion/tumor segmentation through CT images. The SED framework consists of two different encoder-decoder networks connected in series. The first network aims to remove unwanted voxels and organs and to extract liver locations from CT images. The second network uses the results of the first network to further segment the lesions. For practical purpose, the predicted lesions on individual CTs were extracted and reconstructed on 3D images. The experiments conducted on 4300 CT images and LiTS dataset demonstrate that the liver segmentation and the tumor prediction achieved 0.92 and 0.75 in Dice score, respectively, by as-proposed SED method.

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Section: Tumor Segmentatiuon Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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In-Series U-Net Network to 3D Tumor Image Reconstruction for Liver Hepatocellular Carcinoma Recognition

Chen

Liu

et al. 2020

Diagnostics

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“…For example, in patients with liver metastases, the purpose of lesion monitoring is to assess disease progression, stability, or regression over time [23]. In order to quantify the evolution of focal disease, segmentation of focal lesions and the corresponding organ is required to assess the percentage of organ affected by lesions [24]. 6.…”

Section: Overview Of Projectmentioning

confidence: 99%

Deep learning workflow in radiology: a primer

et al. 2020

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Interest for deep learning in radiology has increased tremendously in the past decade due to the high achievable performance for various computer vision tasks such as detection, segmentation, classification, monitoring, and prediction. This article provides step-by-step practical guidance for conducting a project that involves deep learning in radiology, from defining specifications, to deployment and scaling. Specifically, the objectives of this article are to provide an overview of clinical use cases of deep learning, describe the composition of multi-disciplinary team, and summarize current approaches to patient, data, model, and hardware selection. Key ideas will be illustrated by examples from a prototypical project on imaging of colorectal liver metastasis. This article illustrates the workflow for liver lesion detection, segmentation, classification, monitoring, and prediction of tumor recurrence and patient survival. Challenges are discussed, including ethical considerations, cohorting, data collection, anonymization, and availability of expert annotations. The practical guidance may be adapted to any project that requires automated medical image analysis.

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Deep learning and level set approach for liver and tumor segmentation from CT scans

Alirr

2020

J Applied Clin Med Phys

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Purpose Segmentation of liver organ and tumors from computed tomography (CT) scans is an important task for hepatic surgical planning. Manual segmentation of liver and tumors is tedious, time‐consuming, and biased to the clinician experience. Therefore, automatic segmentation of liver and tumors is highly desirable. It would improve the surgical planning treatments and follow‐up assessment. Method This work presented the development of an automatic method for liver and tumor segmentation from CT scans. The proposed method was based on fully convolutional neural (FCN) network with region‐based level set function. The framework starts to segment the liver organ from CT scan, which is followed by a step to segment tumors inside the liver envelope. The fully convolutional network is trained to predict the coarse liver/tumor segmentation, while the localized region‐based level aims to refine the predicted segmentation to find the correct final segmentation. Results The effectiveness of the proposed method is validated against two publically available datasets, LiTS and IRCAD datasets. Dice scores for liver and tumor segmentation in IRCAD datasets are 95.2% and 76.1%, respectively, while for LiTS dataset are 95.6% and 70%, respectively. Conclusion The proposed method succeeded to segment liver and tumors in heterogeneous CT scans from different scanners, as in IRCAD dataset, which proved its ability for generalization and be promising tool for automatic analysis of liver and its tumors in clinical routine.

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A Joint Deep Learning Approach for Automated Liver and Tumor Segmentation

Cited by 37 publications

References 8 publications

In-Series U-Net Network to 3D Tumor Image Reconstruction for Liver Hepatocellular Carcinoma Recognition

In-Series U-Net Network to 3D Tumor Image Reconstruction for Liver Hepatocellular Carcinoma Recognition

Deep learning workflow in radiology: a primer

Deep learning and level set approach for liver and tumor segmentation from CT scans

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