Multi-phase features interaction transformer network for liver tumor segmentation and microvascular invasion assessment in contrast-enhanced CT

Precise segmentation of liver tumors from computed tomography (CT) scans is a prerequisite step in various clinical applications. Multi-phase CT imaging enhances tumor characterization, thereby assisting radiologists in accurate identification. However, existing automatic liver tumor segmentation mo...

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Vydané v:Mathematical biosciences and engineering : MBE Ročník 21; číslo 4; s. 5735 - 5761
Hlavní autori: Zhang, Wencong, Tao, Yuxi, Huang, Zhanyao, Li, Yue, Chen, Yingjia, Song, Tengfei, Ma, Xiangyuan, Zhang, Yaqin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States AIMS Press 24.04.2024
Predmet:
Algorithms
Contrast Media
contrast-enhanced ct
Female
Humans
Image Processing, Computer-Assisted - methods
Liver - blood supply
Liver - diagnostic imaging
Liver - pathology
Liver Neoplasms - blood supply
Liver Neoplasms - diagnostic imaging
Liver Neoplasms - pathology
liver tumor segmentation
Male
Microvessels - diagnostic imaging
Microvessels - pathology
multi-phase
Neoplasm Invasiveness
Radiographic Image Interpretation, Computer-Assisted - methods
Tomography, X-Ray Computed
transformer
liver tumor segmentation
transformer
contrast-enhanced CT
multi-phase
ISSN:1551-0018, 1551-0018
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Abstract Precise segmentation of liver tumors from computed tomography (CT) scans is a prerequisite step in various clinical applications. Multi-phase CT imaging enhances tumor characterization, thereby assisting radiologists in accurate identification. However, existing automatic liver tumor segmentation models did not fully exploit multi-phase information and lacked the capability to capture global information. In this study, we developed a pioneering multi-phase feature interaction Transformer network (MI-TransSeg) for accurate liver tumor segmentation and a subsequent microvascular invasion (MVI) assessment in contrast-enhanced CT images. In the proposed network, an efficient multi-phase features interaction module was introduced to enable bi-directional feature interaction among multiple phases, thus maximally exploiting the available multi-phase information. To enhance the model's capability to extract global information, a hierarchical transformer-based encoder and decoder architecture was designed. Importantly, we devised a multi-resolution scales feature aggregation strategy (MSFA) to optimize the parameters and performance of the proposed model. Subsequent to segmentation, the liver tumor masks generated by MI-TransSeg were applied to extract radiomic features for the clinical applications of the MVI assessment. With Institutional Review Board (IRB) approval, a clinical multi-phase contrast-enhanced CT abdominal dataset was collected that included 164 patients with liver tumors. The experimental results demonstrated that the proposed MI-TransSeg was superior to various state-of-the-art methods. Additionally, we found that the tumor mask predicted by our method showed promising potential in the assessment of microvascular invasion. In conclusion, MI-TransSeg presents an innovative paradigm for the segmentation of complex liver tumors, thus underscoring the significance of multi-phase CT data exploitation. The proposed MI-TransSeg network has the potential to assist radiologists in diagnosing liver tumors and assessing microvascular invasion.
AbstractList Precise segmentation of liver tumors from computed tomography (CT) scans is a prerequisite step in various clinical applications. Multi-phase CT imaging enhances tumor characterization, thereby assisting radiologists in accurate identification. However, existing automatic liver tumor segmentation models did not fully exploit multi-phase information and lacked the capability to capture global information. In this study, we developed a pioneering multi-phase feature interaction Transformer network (MI-TransSeg) for accurate liver tumor segmentation and a subsequent microvascular invasion (MVI) assessment in contrast-enhanced CT images. In the proposed network, an efficient multi-phase features interaction module was introduced to enable bi-directional feature interaction among multiple phases, thus maximally exploiting the available multi-phase information. To enhance the model's capability to extract global information, a hierarchical transformer-based encoder and decoder architecture was designed. Importantly, we devised a multi-resolution scales feature aggregation strategy (MSFA) to optimize the parameters and performance of the proposed model. Subsequent to segmentation, the liver tumor masks generated by MI-TransSeg were applied to extract radiomic features for the clinical applications of the MVI assessment. With Institutional Review Board (IRB) approval, a clinical multi-phase contrast-enhanced CT abdominal dataset was collected that included 164 patients with liver tumors. The experimental results demonstrated that the proposed MI-TransSeg was superior to various state-of-the-art methods. Additionally, we found that the tumor mask predicted by our method showed promising potential in the assessment of microvascular invasion. In conclusion, MI-TransSeg presents an innovative paradigm for the segmentation of complex liver tumors, thus underscoring the significance of multi-phase CT data exploitation. The proposed MI-TransSeg network has the potential to assist radiologists in diagnosing liver tumors and assessing microvascular invasion.Precise segmentation of liver tumors from computed tomography (CT) scans is a prerequisite step in various clinical applications. Multi-phase CT imaging enhances tumor characterization, thereby assisting radiologists in accurate identification. However, existing automatic liver tumor segmentation models did not fully exploit multi-phase information and lacked the capability to capture global information. In this study, we developed a pioneering multi-phase feature interaction Transformer network (MI-TransSeg) for accurate liver tumor segmentation and a subsequent microvascular invasion (MVI) assessment in contrast-enhanced CT images. In the proposed network, an efficient multi-phase features interaction module was introduced to enable bi-directional feature interaction among multiple phases, thus maximally exploiting the available multi-phase information. To enhance the model's capability to extract global information, a hierarchical transformer-based encoder and decoder architecture was designed. Importantly, we devised a multi-resolution scales feature aggregation strategy (MSFA) to optimize the parameters and performance of the proposed model. Subsequent to segmentation, the liver tumor masks generated by MI-TransSeg were applied to extract radiomic features for the clinical applications of the MVI assessment. With Institutional Review Board (IRB) approval, a clinical multi-phase contrast-enhanced CT abdominal dataset was collected that included 164 patients with liver tumors. The experimental results demonstrated that the proposed MI-TransSeg was superior to various state-of-the-art methods. Additionally, we found that the tumor mask predicted by our method showed promising potential in the assessment of microvascular invasion. In conclusion, MI-TransSeg presents an innovative paradigm for the segmentation of complex liver tumors, thus underscoring the significance of multi-phase CT data exploitation. The proposed MI-TransSeg network has the potential to assist radiologists in diagnosing liver tumors and assessing microvascular invasion.
Precise segmentation of liver tumors from computed tomography (CT) scans is a prerequisite step in various clinical applications. Multi-phase CT imaging enhances tumor characterization, thereby assisting radiologists in accurate identification. However, existing automatic liver tumor segmentation models did not fully exploit multi-phase information and lacked the capability to capture global information. In this study, we developed a pioneering multi-phase feature interaction Transformer network (MI-TransSeg) for accurate liver tumor segmentation and a subsequent microvascular invasion (MVI) assessment in contrast-enhanced CT images. In the proposed network, an efficient multi-phase features interaction module was introduced to enable bi-directional feature interaction among multiple phases, thus maximally exploiting the available multi-phase information. To enhance the model's capability to extract global information, a hierarchical transformer-based encoder and decoder architecture was designed. Importantly, we devised a multi-resolution scales feature aggregation strategy (MSFA) to optimize the parameters and performance of the proposed model. Subsequent to segmentation, the liver tumor masks generated by MI-TransSeg were applied to extract radiomic features for the clinical applications of the MVI assessment. With Institutional Review Board (IRB) approval, a clinical multi-phase contrast-enhanced CT abdominal dataset was collected that included 164 patients with liver tumors. The experimental results demonstrated that the proposed MI-TransSeg was superior to various state-of-the-art methods. Additionally, we found that the tumor mask predicted by our method showed promising potential in the assessment of microvascular invasion. In conclusion, MI-TransSeg presents an innovative paradigm for the segmentation of complex liver tumors, thus underscoring the significance of multi-phase CT data exploitation. The proposed MI-TransSeg network has the potential to assist radiologists in diagnosing liver tumors and assessing microvascular invasion.
Author Zhang, Wencong
Huang, Zhanyao
Ma, Xiangyuan
Song, Tengfei
Li, Yue
Chen, Yingjia
Zhang, Yaqin
Tao, Yuxi
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Cites_doi 10.1053/j.gastro.2004.09.011
10.1109/IEMBS.2009.5333760
10.1016/j.simpat.2023.102769
10.1016/j.compbiomed.2017.01.009
10.1007/s00371-021-02328-7
10.1109/ICIP.2019.8802942
10.54294/1uhwld
10.3109/10929080109145999
10.1109/ICCV48922.2021.00468
10.54294/25etax
10.1016/j.compmedimag.2020.101811
10.4236/jcc.2015.311023
10.1002/mp.14922
10.1002/mp.13438
10.1016/j.imed.2022.07.002
10.1109/TMI.2019.2948320
10.1038/s41571-021-00560-7
10.1007/s00261-020-02485-8
10.1038/s41572-020-00240-3
10.1016/j.inffus.2022.10.022
10.1148/radiol.2513081740
10.1016/j.bspc.2023.104791
10.1109/TMI.2018.2878669
10.3390/cancers14122956
10.1109/TIP.2019.2905537
10.1016/j.compmedimag.2021.101885
10.3389/fnins.2022.1009581
10.1158/0008-5472.CAN-17-0339
10.1155/2016/8763205
10.1016/j.compbiomed.2022.105620
10.1007/s00261-022-03496-3
10.1016/j.artmed.2017.03.008
10.1038/s41598-018-33860-7
10.1007/s00268-005-0718-1
10.3389/fonc.2021.588010
10.1007/s00432-020-03366-9
10.1016/j.compeleceng.2013.02.008
10.1109/ACCESS.2021.3107473
10.1016/j.compmedimag.2021.102021
10.3322/caac.21660
10.21037/qims-22-1011
10.1016/j.cmpb.2017.04.008
10.1109/JBHI.2022.3220788
10.3748/wjg.v28.i26.3092
10.1007/s11548-019-01989-z
10.1148/radiol.2273011768
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References key-10.3934/mbe.2024253-38
key-10.3934/mbe.2024253-37
key-10.3934/mbe.2024253-36
key-10.3934/mbe.2024253-35
key-10.3934/mbe.2024253-34
key-10.3934/mbe.2024253-33
key-10.3934/mbe.2024253-32
key-10.3934/mbe.2024253-31
key-10.3934/mbe.2024253-39
key-10.3934/mbe.2024253-41
key-10.3934/mbe.2024253-40
key-10.3934/mbe.2024253-27
key-10.3934/mbe.2024253-26
key-10.3934/mbe.2024253-25
key-10.3934/mbe.2024253-24
key-10.3934/mbe.2024253-23
key-10.3934/mbe.2024253-22
key-10.3934/mbe.2024253-21
key-10.3934/mbe.2024253-20
key-10.3934/mbe.2024253-64
key-10.3934/mbe.2024253-29
key-10.3934/mbe.2024253-28
key-10.3934/mbe.2024253-30
key-10.3934/mbe.2024253-16
key-10.3934/mbe.2024253-15
key-10.3934/mbe.2024253-59
key-10.3934/mbe.2024253-14
key-10.3934/mbe.2024253-58
key-10.3934/mbe.2024253-13
key-10.3934/mbe.2024253-57
key-10.3934/mbe.2024253-12
key-10.3934/mbe.2024253-56
key-10.3934/mbe.2024253-11
key-10.3934/mbe.2024253-55
key-10.3934/mbe.2024253-10
key-10.3934/mbe.2024253-54
key-10.3934/mbe.2024253-53
key-10.3934/mbe.2024253-19
key-10.3934/mbe.2024253-18
key-10.3934/mbe.2024253-17
key-10.3934/mbe.2024253-63
key-10.3934/mbe.2024253-62
key-10.3934/mbe.2024253-61
key-10.3934/mbe.2024253-60
key-10.3934/mbe.2024253-5
key-10.3934/mbe.2024253-49
key-10.3934/mbe.2024253-4
key-10.3934/mbe.2024253-48
key-10.3934/mbe.2024253-3
key-10.3934/mbe.2024253-47
key-10.3934/mbe.2024253-2
key-10.3934/mbe.2024253-46
key-10.3934/mbe.2024253-9
key-10.3934/mbe.2024253-45
key-10.3934/mbe.2024253-8
key-10.3934/mbe.2024253-44
key-10.3934/mbe.2024253-7
key-10.3934/mbe.2024253-43
key-10.3934/mbe.2024253-6
key-10.3934/mbe.2024253-42
key-10.3934/mbe.2024253-52
key-10.3934/mbe.2024253-51
key-10.3934/mbe.2024253-50
key-10.3934/mbe.2024253-1
References_xml – ident: key-10.3934/mbe.2024253-3
  doi: 10.1053/j.gastro.2004.09.011
– ident: key-10.3934/mbe.2024253-18
  doi: 10.1109/IEMBS.2009.5333760
– ident: key-10.3934/mbe.2024253-41
– ident: key-10.3934/mbe.2024253-47
  doi: 10.1016/j.simpat.2023.102769
– ident: key-10.3934/mbe.2024253-16
  doi: 10.1016/j.compbiomed.2017.01.009
– ident: key-10.3934/mbe.2024253-50
  doi: 10.1007/s00371-021-02328-7
– ident: key-10.3934/mbe.2024253-38
  doi: 10.1109/ICIP.2019.8802942
– ident: key-10.3934/mbe.2024253-15
  doi: 10.54294/1uhwld
– ident: key-10.3934/mbe.2024253-55
– ident: key-10.3934/mbe.2024253-14
  doi: 10.3109/10929080109145999
– ident: key-10.3934/mbe.2024253-56
  doi: 10.1109/ICCV48922.2021.00468
– ident: key-10.3934/mbe.2024253-51
– ident: key-10.3934/mbe.2024253-17
  doi: 10.54294/25etax
– ident: key-10.3934/mbe.2024253-21
  doi: 10.1016/j.compmedimag.2020.101811
– ident: key-10.3934/mbe.2024253-59
– ident: key-10.3934/mbe.2024253-7
  doi: 10.4236/jcc.2015.311023
– ident: key-10.3934/mbe.2024253-34
  doi: 10.1002/mp.14922
– ident: key-10.3934/mbe.2024253-24
  doi: 10.1002/mp.13438
– ident: key-10.3934/mbe.2024253-45
  doi: 10.1016/j.imed.2022.07.002
– ident: key-10.3934/mbe.2024253-30
  doi: 10.1109/TMI.2019.2948320
– ident: key-10.3934/mbe.2024253-62
  doi: 10.1038/s41571-021-00560-7
– ident: key-10.3934/mbe.2024253-33
  doi: 10.1007/s00261-020-02485-8
– ident: key-10.3934/mbe.2024253-40
– ident: key-10.3934/mbe.2024253-27
– ident: key-10.3934/mbe.2024253-48
– ident: key-10.3934/mbe.2024253-2
  doi: 10.1038/s41572-020-00240-3
– ident: key-10.3934/mbe.2024253-58
– ident: key-10.3934/mbe.2024253-49
  doi: 10.1016/j.inffus.2022.10.022
– ident: key-10.3934/mbe.2024253-12
  doi: 10.1148/radiol.2513081740
– ident: key-10.3934/mbe.2024253-44
  doi: 10.1016/j.bspc.2023.104791
– ident: key-10.3934/mbe.2024253-20
– ident: key-10.3934/mbe.2024253-22
  doi: 10.1109/TMI.2018.2878669
– ident: key-10.3934/mbe.2024253-9
  doi: 10.3390/cancers14122956
– ident: key-10.3934/mbe.2024253-23
  doi: 10.1109/TIP.2019.2905537
– ident: key-10.3934/mbe.2024253-31
  doi: 10.1016/j.compmedimag.2021.101885
– ident: key-10.3934/mbe.2024253-46
  doi: 10.3389/fnins.2022.1009581
– ident: key-10.3934/mbe.2024253-43
– ident: key-10.3934/mbe.2024253-64
  doi: 10.1158/0008-5472.CAN-17-0339
– ident: key-10.3934/mbe.2024253-4
  doi: 10.1155/2016/8763205
– ident: key-10.3934/mbe.2024253-13
  doi: 10.1016/j.compbiomed.2022.105620
– ident: key-10.3934/mbe.2024253-61
  doi: 10.1007/s00261-022-03496-3
– ident: key-10.3934/mbe.2024253-37
  doi: 10.1016/j.artmed.2017.03.008
– ident: key-10.3934/mbe.2024253-26
  doi: 10.1038/s41598-018-33860-7
– ident: key-10.3934/mbe.2024253-57
– ident: key-10.3934/mbe.2024253-28
– ident: key-10.3934/mbe.2024253-53
– ident: key-10.3934/mbe.2024253-11
  doi: 10.1007/s00268-005-0718-1
– ident: key-10.3934/mbe.2024253-60
  doi: 10.3389/fonc.2021.588010
– ident: key-10.3934/mbe.2024253-10
  doi: 10.1007/s00432-020-03366-9
– ident: key-10.3934/mbe.2024253-19
  doi: 10.1016/j.compeleceng.2013.02.008
– ident: key-10.3934/mbe.2024253-8
  doi: 10.1109/ACCESS.2021.3107473
– ident: key-10.3934/mbe.2024253-54
  doi: 10.1016/j.compmedimag.2021.102021
– ident: key-10.3934/mbe.2024253-1
  doi: 10.3322/caac.21660
– ident: key-10.3934/mbe.2024253-63
  doi: 10.21037/qims-22-1011
– ident: key-10.3934/mbe.2024253-6
  doi: 10.1016/j.cmpb.2017.04.008
– ident: key-10.3934/mbe.2024253-42
– ident: key-10.3934/mbe.2024253-32
  doi: 10.1109/JBHI.2022.3220788
– ident: key-10.3934/mbe.2024253-25
– ident: key-10.3934/mbe.2024253-5
  doi: 10.3748/wjg.v28.i26.3092
– ident: key-10.3934/mbe.2024253-29
– ident: key-10.3934/mbe.2024253-36
  doi: 10.1007/s11548-019-01989-z
– ident: key-10.3934/mbe.2024253-52
– ident: key-10.3934/mbe.2024253-39
– ident: key-10.3934/mbe.2024253-35
  doi: 10.1148/radiol.2273011768
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Snippet Precise segmentation of liver tumors from computed tomography (CT) scans is a prerequisite step in various clinical applications. Multi-phase CT imaging...
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SubjectTerms Algorithms
Contrast Media
contrast-enhanced ct
Female
Humans
Image Processing, Computer-Assisted - methods
Liver - blood supply
Liver - diagnostic imaging
Liver - pathology
Liver Neoplasms - blood supply
Liver Neoplasms - diagnostic imaging
Liver Neoplasms - pathology
liver tumor segmentation
Male
Microvessels - diagnostic imaging
Microvessels - pathology
multi-phase
Neoplasm Invasiveness
Radiographic Image Interpretation, Computer-Assisted - methods
Tomography, X-Ray Computed
transformer
Title Multi-phase features interaction transformer network for liver tumor segmentation and microvascular invasion assessment in contrast-enhanced CT
URI https://www.ncbi.nlm.nih.gov/pubmed/38872556
https://www.proquest.com/docview/3068759424
https://doaj.org/article/8eab0ac54ab347e884eef501e298049c
Volume 21
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