Automated segmentation of liver segment on portal venous phase MR images using a 3D convolutional neural network

Han, Xiao-Jing; Wu, Xinru; Wang, Shuhui; Xu, Lixue; Xu, Hui; Zheng, Dan; Yu, Niange; Hong, Yanjie; Yu, Zhixuan; Yang, Zhenghan

doi:10.1186/s13244-022-01163-1

Cited by 20 publications

(10 citation statements)

References 25 publications

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“…Segment VIII and segment I provided the highest and lowest DSC values in the test data set, respectively. Compared with the methods of Jia et al ( 5 ) and Han et al ( 8 ), ours could obtain higher DSC values. The results are shown in Table 2 .…”

Section: Resultsmentioning

confidence: 55%

“…Several DL models have been developed for the automated segmentation of Couinaud liver segments and preoperative volumetric assessment ( 5 - 8 ). However, these studies mainly concerned technical feasibility.…”

Section: Discussionmentioning

confidence: 99%

“…The performance of our DL model was compared with that of Jia et al ( 5 ) and Han et al ( 8 ) at the functional segment level. The higher DSC values yielded by our model demonstrated its high consistency with human doctors.…”

Section: Discussionmentioning

confidence: 99%

“…Several studies have reported the automated segmentation of Couinaud segments based on deep learning (DL) algorithms; however, these studies aimed to develop a new model and focused on technical feasibility and efficiency ( 5 - 8 ). Variations in attenuation and morphology of the liver (i.e., the presence of hepatic steatosis, cirrhosis, and hepatic tumors) may affect the performance of the DL models in real clinical practice.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning for fully automated segmentation and volumetry of Couinaud liver segments and future liver remnants shown with CT before major hepatectomy: a validation study of a predictive model

Xie¹,

Li²,

Lin³

et al. 2023

Quant Imaging Med Surg

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Background Recent reports have shown the potential for deep learning (DL) models to automatically segment of Couinaud liver segments and future liver remnant (FLR) for liver resections. However, these studies have mainly focused on the development of the models. Existing reports lack adequate validation of these models in diverse liver conditions and thorough evaluation using clinical cases. This study thus aimed to develop and perform a spatial external validation of a DL model for the automated segmentation of Couinaud liver segments and FLR using computed tomography (CT) in various liver conditions and to apply the model prior to major hepatectomy. Methods This retrospective study developed a 3-dimensional (3D) U-Net model for the automated segmentation of Couinaud liver segments and FLR on contrast-enhanced portovenous phase (PVP) CT scans. Images were obtained from 170 patients from January 2018 to March 2019. First, radiologists annotated the Couinaud segmentations. Then, a 3D U-Net model was trained in Peking University First Hospital (n=170) and tested in Peking University Shenzhen Hospital (n=178) in cases with various liver conditions (n=146) and in candidates for major hepatectomy (n=32). The segmentation accuracy was evaluated using the dice similarity coefficient (DSC). Quantitative volumetry to evaluate the resectability was compared between manual and automated segmentation. Results The DSC in the test data sets 1 and 2 for segments I to VIII was 0.93±0.01, 0.94±0.01, 0.93±0.01, 0.93±0.01, 0.94±0.00, 0.95±0.00, 0.95±0.00, and 0.95±0.00, respectively. The mean automated FLR and FLR% assessments were 493.51±284.77 mL and 38.53%±19.38%, respectively. The mean manual FLR and FLR% assessments were 500.92±284.38 mL and 38.35%±19.14%, respectively, in test data sets 1 and 2. For test data set 1, when automated segmentation of the FLR% was used, 106, 23, 146, and 57 cases were categorized as candidates for a virtual major hepatectomy of types 1, 2, 3, and 4, respectively; however, when manual segmentation of the FLR% was used, 107, 23, 146, and 57 cases were categorized as candidates for a virtual major hepatectomy of types 1, 2, 3, and 4, respectively. For test data set 2, all cases were categorized as candidates for major hepatectomy when automated and manual segmentation of the FLR% was used. No significant differences in FLR assessment (P=0.50; U=185,545), FLR% assessment (P=0.82; U=188,337), or the indications for major hepatectomy were noted between automated and manual segmentation (McNemar test statistic 0.00; P>0.99). Conclusions The DL model could be used to fully automate the segmentation of Couinaud liver segments and FLR with CT prior to major hepatectomy in an accurate and clinically practicable manner.

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Section: Resultsmentioning

confidence: 55%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep learning for fully automated segmentation and volumetry of Couinaud liver segments and future liver remnants shown with CT before major hepatectomy: a validation study of a predictive model

Xie¹,

Li²,

Lin³

et al. 2023

Quant Imaging Med Surg

View full text Add to dashboard Cite

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“…The key challenge for naïve segmentation models is the lack of distinct boundaries and edge information between any two adjacent subregions. Deep learning networks have been used for the zonal segmentation of several anatomical structures [ 18 , 19 , 20 ]. The performance of these techniques largely depends on the segmentation approach adopted.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Pancreatic Subregions in Computed Tomography Images

et al. 2022

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The accurate segmentation of pancreatic subregions (head, body, and tail) in CT images provides an opportunity to examine the local morphological and textural changes in the pancreas. Quantifying such changes aids in understanding the spatial heterogeneity of the pancreas and assists in the diagnosis and treatment planning of pancreatic cancer. Manual outlining of pancreatic subregions is tedious, time-consuming, and prone to subjective inconsistency. This paper presents a multistage anatomy-guided framework for accurate and automatic 3D segmentation of pancreatic subregions in CT images. Using the delineated pancreas, two soft-label maps were estimated for subregional segmentation—one by training a fully supervised naïve Bayes model that considers the length and volumetric proportions of each subregional structure based on their anatomical arrangement, and the other by using the conventional deep learning U-Net architecture for 3D segmentation. The U-Net model then estimates the joint probability of the two maps and performs optimal segmentation of subregions. Model performance was assessed using three datasets of contrast-enhanced abdominal CT scans: one public NIH dataset of the healthy pancreas, and two datasets D1 and D2 (one for each of pre-cancerous and cancerous pancreas). The model demonstrated excellent performance during the multifold cross-validation using the NIH dataset, and external validation using D1 and D2. To the best of our knowledge, this is the first automated model for the segmentation of pancreatic subregions in CT images. A dataset consisting of reference anatomical labels for subregions in all images of the NIH dataset is also established.

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Deep Learning–Based Approach for Identifying and Measuring Focal Liver Lesions on Contrast‐Enhanced MRI

Dai,

Xiao,

et al. 2024

Magnetic Resonance Imaging

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BackgroundThe number of focal liver lesions (FLLs) detected by imaging has increased worldwide, highlighting the need to develop a robust, objective system for automatically detecting FLLs.PurposeTo assess the performance of the deep learning–based artificial intelligence (AI) software in identifying and measuring lesions on contrast‐enhanced magnetic resonance imaging (MRI) images in patients with FLLs.Study TypeRetrospective.Subjects395 patients with 1149 FLLs.Field Strength/SequenceThe 1.5 T and 3 T scanners, including T1‐, T2‐, diffusion‐weighted imaging, in/out‐phase imaging, and dynamic contrast‐enhanced imaging.AssessmentThe diagnostic performance of AI, radiologist, and their combination was compared. Using 20 mm as the cut‐off value, the lesions were divided into two groups, and then divided into four subgroups: <10, 10–20, 20–40, and ≥40 mm, to evaluate the sensitivity of radiologists and AI in the detection of lesions of different sizes. We compared the pathologic sizes of 122 surgically resected lesions with measurements obtained using AI and those made by radiologists.Statistical TestsMcNemar test, Bland–Altman analyses, Friedman test, Pearson's chi‐squared test, Fisher's exact test, Dice coefficient, and intraclass correlation coefficients. A P‐value <0.05 was considered statistically significant.ResultsThe average Dice coefficient of AI in segmentation of liver lesions was 0.62. The combination of AI and radiologist outperformed the radiologist alone, with a significantly higher detection rate (0.894 vs. 0.825) and sensitivity (0.883 vs. 0.806). The AI showed significantly sensitivity than radiologists in detecting all lesions <20 mm (0.848 vs. 0.788). Both AI and radiologists achieved excellent detection performance for lesions ≥20 mm (0.867 vs. 0.881, P = 0.671). A remarkable agreement existed in the average tumor sizes among the three measurements (P = 0.174).Data ConclusionAI software based on deep learning exhibited practical value in automatically identifying and measuring liver lesions.Level of Evidence4.Technical EfficacyStage 2.

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Automated segmentation of liver segment on portal venous phase MR images using a 3D convolutional neural network

Cited by 20 publications

References 25 publications

Deep learning for fully automated segmentation and volumetry of Couinaud liver segments and future liver remnants shown with CT before major hepatectomy: a validation study of a predictive model

Deep learning for fully automated segmentation and volumetry of Couinaud liver segments and future liver remnants shown with CT before major hepatectomy: a validation study of a predictive model

Segmentation of Pancreatic Subregions in Computed Tomography Images

Deep Learning–Based Approach for Identifying and Measuring Focal Liver Lesions on Contrast‐Enhanced MRI

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