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Comparative evaluation of volumetry estimation from plain and contrast enhanced computed tomography liver images

  • ORCID logo , , EMAIL logo und
Veröffentlicht/Copyright: 6. Mai 2024
Biomedical Engineering / Biomedizinische Technik
Aus der Zeitschrift Biomedical Engineering / Biomedizinische Technik Band 69 Heft 5

Abstract

Objectives

Surgery planning for liver tumour is carried out using contrast enhanced computed tomography (CECT) images to determine the optimal resection strategy and to assess the volume of liver and tumour. Current surgery planning tools interpret even the functioning liver cells present within the tumour boundary as tumour. Plain CT images provide inadequate information for treatment planning. This work attempts to address two shortcomings of existing surgery planning tools: (i) to delineate functioning liver cells from the non-functioning tumourous tissues within the tumour boundary and (ii) to provide 3D visualization and actual tumour volume from the plain CT images.

Methods

All slices of plain CT images containing liver are enhanced by means of fuzzy histogram equalization in Non-Subsampled Contourlet Transform (NSCT) domain prior to 3D reconstruction to clearly delineate liver, non-functioning tumourous tissues and functioning liver cells within the tumour boundary. The 3D analysis from plain and CECT images was carried out on five types of liver lesions viz. HCC, metastasis, hemangioma, cyst, and abscess along with normal liver.

Results

The study resulted in clear delineation of functional liver tissues from non-functioning tumourous tissues within the tumour boundary from CECT as well as plain CT images. The volume of liver calculated using the proposed approach is found comparable with that obtained using Myrian-XP, a currently followed surgery planning tool in clinical practice.

Conclusions

The obtained results from plain CT images will undoubtedly provide valuable diagnostic assistance and surgery planning even for the subset of patients for whom CECT acquisition is not advisable.

Keywords: 3D visualization; computed tomography; liver cancer; surgery planning; volumetry estimation
Corresponding author: Dr. Biju Pottakkat, Professor, Department of Surgical Gastroenterology, Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER), Puducherry 605006, India, E-mail:

Funding source: Science and Engineering Research Board

Award Identifier / Grant number: PDF/2020/002607

Funding source: University Grants Commission

Award Identifier / Grant number: F 6684/16

  1. Research ethics: This was a retrospective study conducted as a single centre study in the Department of Surgical Gastroenterology and Department of Radio-Diagnosis at Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER), Puducherry, India. The study was approved by the institutional research review board (JIP/93/2021/042) and institute ethics committee for human studies (JIP/IEC/2021/248).

  2. Informed consent: Not Applicable as this is only a retrospective study and no patients are recruited for the study.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Competing interests: Authors state no conflict of interest.

  5. Research funding: This study has been financially supported by the University Grants Commission (UGC), India under Minor Research Project (ROMRP) scheme [Grant No. F 6684/16]. The software Simpleware ScanIP software used in this work was procured under this research grant. This study is also financially supported by Science and Engineering Research Board (SERB), India under National Post-Doctoral Fellowship (NPDF) scheme (PDF/2020/002607).

  6. Data availablity: Dataset used in this work is an institutional dataset belonging to JIPMER, Puducherry, India, which is confidential.

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Received: 2022-09-02
Accepted: 2024-04-15
Published Online: 2024-05-06
Published in Print: 2024-10-28

© 2024 Walter de Gruyter GmbH, Berlin/Boston

Artikel in diesem Heft

  1. Frontmatter
  2. Research Articles
  3. Sparse-view CT reconstruction based on group-based sparse representation using weighted guided image filtering
  4. Comparative evaluation of volumetry estimation from plain and contrast enhanced computed tomography liver images
  5. Vein segmentation and visualization of upper and lower extremities using convolution neural network
  6. STF-Net: sparsification transformer coding guided network for subcortical brain structure segmentation
  7. Machine learning based endothelial cell image analysis of patients undergoing descemet membrane endothelial keratoplasty surgery
  8. AML leukocyte classification method for small samples based on ACGAN
  9. Deep learning classification of EEG-based BCI monitoring of the attempted arm and hand movements
  10. Hybrid method for noise rejection from breath sound using transient artifact reduction algorithm and spectral subtraction
  11. Optimized Schlieren imaging for real-time visualization of high-intensity focused ultrasound waves
https://doi.org/10.1515/bmt-2022-0349
Schlagwörter für diesen Artikel
3D visualization; computed tomography; liver cancer; surgery planning; volumetry estimation

Artikel in diesem Heft

  1. Frontmatter
  2. Research Articles
  3. Sparse-view CT reconstruction based on group-based sparse representation using weighted guided image filtering
  4. Comparative evaluation of volumetry estimation from plain and contrast enhanced computed tomography liver images
  5. Vein segmentation and visualization of upper and lower extremities using convolution neural network
  6. STF-Net: sparsification transformer coding guided network for subcortical brain structure segmentation
  7. Machine learning based endothelial cell image analysis of patients undergoing descemet membrane endothelial keratoplasty surgery
  8. AML leukocyte classification method for small samples based on ACGAN
  9. Deep learning classification of EEG-based BCI monitoring of the attempted arm and hand movements
  10. Hybrid method for noise rejection from breath sound using transient artifact reduction algorithm and spectral subtraction
  11. Optimized Schlieren imaging for real-time visualization of high-intensity focused ultrasound waves
Heruntergeladen am 19.4.2026 von https://www.degruyterbrill.com/document/doi/10.1515/bmt-2022-0349/html
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