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Li S, Li X, Zhou F, Zhang Y, Bie Z, Cheng L, Peng J, Li B. Automated segmentation of liver and hepatic vessels on portal venous phase computed tomography images using a deep learning algorithm. J Appl Clin Med Phys 2024; 25:e14397. [PMID: 38773719 PMCID: PMC11302809 DOI: 10.1002/acm2.14397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 04/20/2024] [Accepted: 04/29/2024] [Indexed: 05/24/2024] Open
Abstract
BACKGROUND CT-image segmentation for liver and hepatic vessels can facilitate liver surgical planning. However, time-consuming process and inter-observer variations of manual segmentation have limited wider application in clinical practice. PURPOSE Our study aimed to propose an automated deep learning (DL) segmentation algorithm for liver and hepatic vessels on portal venous phase CT images. METHODS This retrospective study was performed to develop a coarse-to-fine DL-based algorithm that was trained, validated, and tested using private 413, 52, and 50 portal venous phase CT images, respectively. Additionally, the performance of the DL algorithm was extensively evaluated and compared with manual segmentation using an independent clinical dataset of preoperative contrast-enhanced CT images from 44 patients with hepatic focal lesions. The accuracy of DL-based segmentation was quantitatively evaluated using the Dice Similarity Coefficient (DSC) and complementary metrics [Normalized Surface Dice (NSD) and Hausdorff distance_95 (HD95) for liver segmentation, Recall and Precision for hepatic vessel segmentation]. The processing time for DL and manual segmentation was also compared. RESULTS Our DL algorithm achieved accurate liver segmentation with DSC of 0.98, NSD of 0.92, and HD95 of 1.52 mm. DL-segmentation of hepatic veins, portal veins, and inferior vena cava attained DSC of 0.86, 0.89, and 0.94, respectively. Compared with the manual approach, the DL algorithm significantly outperformed with better segmentation results for both liver and hepatic vessels, with higher accuracy of liver and hepatic vessel segmentation (all p < 0.001) in independent 44 clinical data. In addition, the DL method significantly reduced the manual processing time of clinical postprocessing (p < 0.001). CONCLUSIONS The proposed DL algorithm potentially enabled accurate and rapid segmentation for liver and hepatic vessels using portal venous phase contrast CT images.
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Affiliation(s)
- Shengwei Li
- Minimally Invasive Tumor Therapy CenterBeijing HospitalPeking Union Medical CollegeBeijingChina
| | - Xiao‐Guang Li
- Minimally Invasive Tumor Therapy CenterBeijing HospitalPeking Union Medical CollegeBeijingChina
| | - Fanyu Zhou
- Minimally Invasive Tumor Therapy CenterBeijing HospitalPeking Union Medical CollegeBeijingChina
| | - Yumeng Zhang
- Minimally Invasive Tumor Therapy CenterBeijing HospitalPeking Union Medical CollegeBeijingChina
| | - Zhixin Bie
- Minimally Invasive Tumor Therapy CenterBeijing HospitalPeking Union Medical CollegeBeijingChina
| | - Lin Cheng
- Minimally Invasive Tumor Therapy CenterBeijing HospitalPeking Union Medical CollegeBeijingChina
| | - Jinzhao Peng
- Minimally Invasive Tumor Therapy CenterBeijing HospitalPeking Union Medical CollegeBeijingChina
| | - Bin Li
- Minimally Invasive Tumor Therapy CenterBeijing HospitalPeking Union Medical CollegeBeijingChina
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Czipczer V, Manno-Kovacs A. Adaptable volumetric liver segmentation model for CT images using region-based features and convolutional neural network. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.07.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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3
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Farzaneh N, Stein EB, Soroushmehr R, Gryak J, Najarian K. A deep learning framework for automated detection and quantitative assessment of liver trauma. BMC Med Imaging 2022; 22:39. [PMID: 35260105 PMCID: PMC8905785 DOI: 10.1186/s12880-022-00759-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 02/17/2022] [Indexed: 11/19/2022] Open
Abstract
Background Both early detection and severity assessment of liver trauma are critical for optimal triage and management of trauma patients. Current trauma protocols utilize computed tomography (CT) assessment of injuries in a subjective and qualitative (v.s. quantitative) fashion, shortcomings which could both be addressed by automated computer-aided systems that are capable of generating real-time reproducible and quantitative information. This study outlines an end-to-end pipeline to calculate the percentage of the liver parenchyma disrupted by trauma, an important component of the American Association for the Surgery of Trauma (AAST) liver injury scale, the primary tool to assess liver trauma severity at CT. Methods This framework comprises deep convolutional neural networks that first generate initial masks of both liver parenchyma (including normal and affected liver) and regions affected by trauma using three dimensional contrast-enhanced CT scans. Next, during the post-processing step, human domain knowledge about the location and intensity distribution of liver trauma is integrated into the model to avoid false positive regions. After generating the liver parenchyma and trauma masks, the corresponding volumes are calculated. Liver parenchymal disruption is then computed as the volume of the liver parenchyma that is disrupted by trauma. Results The proposed model was trained and validated on an internal dataset from the University of Michigan Health System (UMHS) including 77 CT scans (34 with and 43 without liver parenchymal trauma). The Dice/recall/precision coefficients of the proposed segmentation models are 96.13/96.00/96.35% and 51.21/53.20/56.76%, respectively, in segmenting liver parenchyma and liver trauma regions. In volume-based severity analysis, the proposed model yields a linear regression relation of 0.95 in estimating the percentage of liver parenchyma disrupted by trauma. The model shows an accurate performance in avoiding false positives for patients without any liver parenchymal trauma. These results indicate that the model is generalizable on patients with pre-existing liver conditions, including fatty livers and congestive hepatopathy. Conclusion The proposed algorithms are able to accurately segment the liver and the regions affected by trauma. This pipeline demonstrates an accurate performance in estimating the percentage of liver parenchyma that is affected by trauma. Such a system can aid critical care medical personnel by providing a reproducible quantitative assessment of liver trauma as an alternative to the sometimes subjective AAST grading system that is used currently. Supplementary Information The online version contains supplementary material available at 10.1186/s12880-022-00759-9.
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Affiliation(s)
- Negar Farzaneh
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA. .,The Max Harry Weil Institute for Critical Care Research & Innovation, University of Michigan, Ann Arbor, MI, 48109, USA. .,Department of Emergency Medicine, University of Michigan, Ann Arbor, MI, 48109, USA.
| | - Erica B Stein
- Department of Radiology, Michigan Medicine, Ann Arbor, MI, 48109, USA
| | - Reza Soroushmehr
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA.,The Max Harry Weil Institute for Critical Care Research & Innovation, University of Michigan, Ann Arbor, MI, 48109, USA.,Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, MI, 48109, USA
| | - Jonathan Gryak
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA.,Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, MI, 48109, USA
| | - Kayvan Najarian
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA.,The Max Harry Weil Institute for Critical Care Research & Innovation, University of Michigan, Ann Arbor, MI, 48109, USA.,Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, MI, 48109, USA.,Department of Emergency Medicine, University of Michigan, Ann Arbor, MI, 48109, USA.,Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA
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Messaoudi R, Jaziri F, Mtibaa A, Gargouri F, Vacavant A. Ontology-Driven Approach for Liver MRI Classification and HCC Detection. INT J PATTERN RECOGN 2021. [DOI: 10.1142/s0218001421600077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Reading and interpreting the medical image still remains the most challenging task in radiology. Through the important achievement of deep Convolutional Neural Networks (CNN) in the context of medical image classification, various clinical applications have been provided to detect lesions from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans. In the diagnosis process for the liver cancer from Dynamic Contrast-Enhanced MRI (DCE-MRI), radiologists consider three phases during contrast injection: before injection, arterial phase, and portal phase for instance. Even if the contrast agent helps in enhancing the tumoral tissues, the diagnosis may be very difficult due to the possible low contrast and pathological tissues surrounding the tumors (cirrhosis). Alongside, in the medical field, ontologies have proven their effectiveness to solve several clinical problems such as offering shareable terminologies, vocabularies, and databases. In this article, we propose a multi-label CNN classification approach based on a parallel preprocessing algorithm. This algorithm is an extension of our previous work cited in the International Conference on Pattern Recognition and Artificial Intelligence (ICPRAI) 2020. The aim of our approach is to ameliorate the detection of HCC lesions and to extract more information about the detected tumor such as the stage, the localization, the size, and the type thanks to the use of ontologies. Moreover, the integration of such information has improved the detection process. In fact, experiments conducted by testing with real patient cases have shown that the proposed approach reached an accuracy of 93% using MRI patches of [Formula: see text] pixels, which is an improvement compared with our previous works.
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Affiliation(s)
- Rim Messaoudi
- MIRACL Laboratory, University of Sfax, Sfax, Tunisia
- Institut Pascal, Université Clermont Auvergne, UMR6602, CNRS/UCA/SIGMA, 63171 Aubière, France
| | - Faouzi Jaziri
- Institut Pascal, Université Clermont Auvergne, UMR6602, CNRS/UCA/SIGMA, 63171 Aubière, France
| | - Achraf Mtibaa
- MIRACL Laboratory, University of Sfax, Sfax, Tunisia
- National School of Electronic and Telecommunications, University of Sfax, Sfax, Tunisia
| | - Faïez Gargouri
- MIRACL Laboratory, University of Sfax, Sfax, Tunisia
- Higher Institute of Computer Science and Multimedia, University of Sfax, Sfax, Tunisia
| | - Antoine Vacavant
- Institut Pascal, Université Clermont Auvergne, UMR6602, CNRS/UCA/SIGMA, 63171 Aubière, France
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Heidari M, Taghizadeh M, Masoumi H, Valizadeh M. Liver Segmentation in MRI Images using an Adaptive Water Flow Model. J Biomed Phys Eng 2021; 11:527-534. [PMID: 34458200 PMCID: PMC8385226 DOI: 10.31661/jbpe.v0i0.2103-1293] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 06/18/2021] [Indexed: 12/26/2022]
Abstract
Background: Identification and precise localization of the liver surface and its segments are essential for any surgical treatment. An algorithm of accurate liver segmentation simplifies the treatment
planning for different types of liver diseases. Although liver segmentation turns researcher’s attention, it still has some challenging problems in computer-aided diagnosis. Objective: This study aimed to extract the potential liver regions by an adaptive water flow model and perform the final segmentation by the classification algorithm. Material and Methods: In this experimental study, an automatic liver segmentation algorithm was introduced. The proposed method designed the image by a transfer function based on
the probability distribution function of the liver pixels to enhance the liver area. The enhanced image is then segmented using an adaptive water flow model in which
the rainfall process is controlled by the liver location in the training images and the gray levels of pixels. The candidate liver segments are classified by
a Multi-Layer Perception (MLP) neural network considering some texture, area, and gray level features. Results: The proposed algorithm efficiently distinguishes the liver region from its surrounding organs, resulting in perfect liver segmentation over 250 Magnetic Resonance
Imaging (MRI) test images. The accuracy of 97% was obtained by quantitative evaluation over test images, which revealed the superiority of the proposed algorithm
compared to some evaluated algorithms. Conclusion: Liver segmentation using an adaptive water flow algorithm and classifying the segmented area in MRI images yields more robust and reliable results in comparison with the classification of pixels.
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Affiliation(s)
- Marjan Heidari
- PhD candidate, Department of Biomedical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran
| | - Mehdi Taghizadeh
- PhD, Department of Electrical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran
| | - Hassan Masoumi
- PhD, Department of Biomedical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran
| | - Morteza Valizadeh
- PhD, Department of Electrical and Computer Engineering, Urmia University, Urmia, Iran
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Haniff NSM, Abdul Karim MK, Osman NH, Saripan MI, Che Isa IN, Ibahim MJ. Stability and Reproducibility of Radiomic Features Based Various Segmentation Technique on MR Images of Hepatocellular Carcinoma (HCC). Diagnostics (Basel) 2021; 11:diagnostics11091573. [PMID: 34573915 PMCID: PMC8468357 DOI: 10.3390/diagnostics11091573] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 08/21/2021] [Accepted: 08/23/2021] [Indexed: 01/04/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is considered as a complex liver disease and ranked as the eighth-highest mortality rate with a prevalence of 2.4% in Malaysia. Magnetic resonance imaging (MRI) has been acknowledged for its advantages, a gold technique for diagnosing HCC, and yet the false-negative diagnosis from the examinations is inevitable. In this study, 30 MR images from patients diagnosed with HCC is used to evaluate the robustness of semi-automatic segmentation using the flood fill algorithm for quantitative features extraction. The relevant features were extracted from the segmented MR images of HCC. Four types of features extraction were used for this study, which are tumour intensity, shape feature, textural feature and wavelet feature. A total of 662 radiomic features were extracted from manual and semi-automatic segmentation and compared using intra-class relation coefficient (ICC). Radiomic features extracted using semi-automatic segmentation utilized flood filling algorithm from 3D-slicer had significantly higher reproducibility (average ICC = 0.952 ± 0.009, p < 0.05) compared with features extracted from manual segmentation (average ICC = 0.897 ± 0.011, p > 0.05). Moreover, features extracted from semi-automatic segmentation were more robust compared to manual segmentation. This study shows that semi-automatic segmentation from 3D-Slicer is a better alternative to the manual segmentation, as they can produce more robust and reproducible radiomic features.
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Affiliation(s)
- Nurin Syazwina Mohd Haniff
- Department of Physics, Faculty of Science, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia; (N.S.M.H.); (N.H.O.)
| | - Muhammad Khalis Abdul Karim
- Department of Physics, Faculty of Science, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia; (N.S.M.H.); (N.H.O.)
- Correspondence:
| | - Nurul Huda Osman
- Department of Physics, Faculty of Science, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia; (N.S.M.H.); (N.H.O.)
| | - M Iqbal Saripan
- Department of Computer Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia;
| | - Iza Nurzawani Che Isa
- Programme of Diagnostic Imaging and Radiotherapy, Universiti Kebangsaan Malaysia, Kuala Lumpur 50300, Wilayah Persekutuan, Malaysia;
| | - Mohammad Johari Ibahim
- Department Biochemistry & Molecular Medicine, Faculty of Medicine, Universiti Teknologi MARA, Sungai Buloh 47200, Selangor, Malaysia;
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Abstract
Accurate liver vessel segmentation is of crucial importance for the clinical diagnosis and treatment of many hepatic diseases. Recent state-of-the-art methods for liver vessel reconstruction mostly utilize deep learning methods, namely, the U-Net model and its variants. However, to the best of our knowledge, no comparative evaluation has been proposed to compare these approaches in the liver vessel segmentation task. Moreover, most research works do not consider the liver volume segmentation as a preprocessing step, in order to keep only inner hepatic vessels, for Couinaud representation for instance. For these reasons, in this work, we propose using accurate Dense U-Net liver segmentation and conducting a comparison between 3D U-Net models inside the obtained volumes. More precisely, 3D U-Net, Dense U-Net, and MultiRes U-Net are pitted against each other in the vessel segmentation task on the IRCAD dataset. For each model, three alternative setups that allow adapting the selected CNN architectures to volumetric data are tested, namely, full 3D, slab-based, and box-based setups are considered. The results showed that the most accurate setup is the full 3D process, providing the highest Dice for most of the considered models. However, concerning the particular models, the slab-based MultiRes U-Net provided the best score. With our accurate vessel segmentations, several medical applications can be investigated, such as automatic and personalized Couinaud zoning of the liver.
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