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AuthorHossain, Md Sakib Abrar
AuthorGul, Sidra
AuthorChowdhury, Muhammad E.H.
AuthorKhan, Muhammad Salman
AuthorSumon, Md Shaheenur Islam
AuthorBhuiyan, Enamul Haque
AuthorKhandakar, Amith
AuthorHossain, Maqsud
AuthorSadique, Abdus
AuthorAl-Hashimi, Israa
AuthorAyari, Mohamed Arselene
AuthorMahmud, Sakib
AuthorAlqahtani, Abdulrahman
Available date2024-04-22T09:00:46Z
Publication Date2023-11-01
Publication NameSensors (Basel, Switzerland)
Identifierhttp://dx.doi.org/10.3390/s23218890
CitationHossain, M. S. A., Gul, S., Chowdhury, M. E., Khan, M. S., Sumon, M. S. I., Bhuiyan, E. H., ... & Alqahtani, A. (2023). Deep Learning Framework for Liver Segmentation from T 1-Weighted MRI Images. Sensors, 23(21), 8890.
ISSN1424-8220
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85176906766&origin=inward
URIhttp://hdl.handle.net/10576/54043
AbstractThe human liver exhibits variable characteristics and anatomical information, which is often ambiguous in radiological images. Machine learning can be of great assistance in automatically segmenting the liver in radiological images, which can be further processed for computer-aided diagnosis. Magnetic resonance imaging (MRI) is preferred by clinicians for liver pathology diagnosis over volumetric abdominal computerized tomography (CT) scans, due to their superior representation of soft tissues. The convenience of Hounsfield unit (HoU) based preprocessing in CT scans is not available in MRI, making automatic segmentation challenging for MR images. This study investigates multiple state-of-the-art segmentation networks for liver segmentation from volumetric MRI images. Here, T1-weighted (in-phase) scans are investigated using expert-labeled liver masks from a public dataset of 20 patients (647 MR slices) from the Combined Healthy Abdominal Organ Segmentation grant challenge (CHAOS). The reason for using T1-weighted images is that it demonstrates brighter fat content, thus providing enhanced images for the segmentation task. Twenty-four different state-of-the-art segmentation networks with varying depths of dense, residual, and inception encoder and decoder backbones were investigated for the task. A novel cascaded network is proposed to segment axial liver slices. The proposed framework outperforms existing approaches reported in the literature for the liver segmentation task (on the same test set) with a dice similarity coefficient (DSC) score and intersect over union (IoU) of 95.15% and 92.10%, respectively.
SponsorThis research was funded by Qatar University High Impact grant QUHI-CENG-23/24-216 and student grant QUST-1-CENG-2023-796 and is also supported via funding from Prince Sattam Bin Abdulaziz University project number (PSAU/2023/R/1444). The open-access publication cost is covered by the Qatar National Library.
Languageen
PublisherMultidisciplinary Digital Publishing Institute (MDPI)
Subjectautomated liver segmentation
deep learning
diagnostic radiology
MRI
T1-weighted contrast
TitleDeep Learning Framework for Liver Segmentation from T1-Weighted MRI Images
TypeArticle
Issue Number21
Volume Number23
ESSN1424-8220
dc.accessType Open Access


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