title: A novel semi-automatic segmentation method for volumetric assessment of the colon based on magnetic resonance imaging.
abstract: OBJECTIVE
  To develop a novel semi-automatic segmentation method for quantification of the colon from magnetic resonance imaging (MRI).
  
  
  METHODS
  Fourteen abdominal T2-weighted and dual-echo Dixon-type water-only MRI scans were obtained from four healthy subjects. Regions of interest containing the colon were outlined manually on the T2-weighted images. Segmentation of the colon and feces was obtained using k-means clustering and image registration. Regional colonic and fecal volumes were obtained. Inter-observer agreement between two observers was assessed using the Dice similarity coefficient as measure of overlap.
  
  
  RESULTS
  Colonic segmentations showed wide variation in volume and morphology between subjects. Colon volumes of the four healthy subjects for both observers were (median [interquartile range]) ascending colon 200 mL [169.5-260], transverse 200.5 mL [113.5-242.5], descending 148 mL [121.5-178.5], sigmoid-rectum 277 mL [192-345], and total 819 mL [687-898.5]. Overlap agreement for the total colon segmentation between the two observers was high with a Dice similarity coefficient of 0.91 [0.84-0.94]. The colon volume to feces volume ratio was on average 0.7.
  
  
  CONCLUSIONS
  Regional colon volumes were comparable to previous findings using fully manual segmentation. The method showed good agreement between observers and may be used in future studies of gastrointestinal disorders to assess colon and fecal volume and colon morphology. Novel insight into morphology and quantitative assessment of the colon using this method may provide new biomarkers for constipation and abdominal pain compared to radiography which suffers from poor reliability.
authors.affiliation: Mech-Sense, Department of Gastroenterology & Hepatology, Aalborg University Hospital, Mølleparkvej 4, 9000, Aalborg, Denmark.
authors.forename: Thomas Holm
authors.initials: TH
authors.lastname: Sandberg
authors.affiliation: Mech-Sense, Department of Gastroenterology & Hepatology, Aalborg University Hospital, Mølleparkvej 4, 9000, Aalborg, Denmark.
authors.forename: Matias
authors.initials: M
authors.lastname: Nilsson
authors.affiliation: Mech-Sense, Department of Gastroenterology & Hepatology, Aalborg University Hospital, Mølleparkvej 4, 9000, Aalborg, Denmark.
authors.forename: Jakob Lykke
authors.initials: JL
authors.lastname: Poulsen
authors.affiliation: Mech-Sense, Department of Gastroenterology & Hepatology, Aalborg University Hospital, Mølleparkvej 4, 9000, Aalborg, Denmark.
authors.forename: Mikkel
authors.initials: M
authors.lastname: Gram
authors.affiliation: Mech-Sense, Department of Radiology, Aalborg University Hospital, Aalborg, Denmark.|Department of Clinical Medicine, Aalborg University, Aalborg, Denmark.
authors.forename: Jens Brøndum
authors.initials: JB
authors.lastname: Frøkjær
authors.affiliation: Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
authors.forename: Lasse Riis
authors.initials: LR
authors.lastname: Østergaard
authors.affiliation: Mech-Sense, Department of Gastroenterology & Hepatology, Aalborg University Hospital, Mølleparkvej 4, 9000, Aalborg, Denmark. amd@rn.dk.|Department of Clinical Medicine, Aalborg University, Aalborg, Denmark. amd@rn.dk.
authors.forename: Asbjørn Mohr
authors.initials: AM
authors.lastname: Drewes
mesh_terms: D000328:Adult; D003106:Colon / Q000033:anatomy & histology*; D006801:Humans; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging; D008297:Male; D015588:Observer Variation; D012016:Reference Values; D015203:Reproducibility of Results
keywords: Artificial intelligence; Colon; Computer-assisted image processing; Constipation; Machine learning; Magnetic resonance imaging