A computer-assisted model for detection of MRI signs of Crohn’s disease activity: future or fiction?

Tielbeek, Jeroen A. W.; Vos, Frans M.; Stoker, Jaap

doi:10.1007/s00261-011-9822-x

Cited by 13 publications

(11 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Crohn's disease lesion finding methods from MRI images are previously proposed. [2][3][4] Mahapatra et al 2 detect lesions from MRI volumes. They classify small VOIs in a MRI volume into diseased intestine, normal intestine, and nonintestine tissue by using the random forest classifier.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Connection method of separated luminal regions of intestine from CT volumes

et al. 2015

View full text Add to dashboard Cite

This paper proposes a connection method of separated luminal regions of the intestine for Crohn's disease diagnosis. Crohn's disease is an inflammatory disease of the digestive tract. Capsule or conventional endoscopic diagnosis is performed for Crohn's disease diagnosis. However, parts of the intestines may not be observed in the endoscopic diagnosis if intestinal stenosis occurs. Endoscopes cannot pass through the stenosed parts. CT imagebased diagnosis is developed as an alternative choice of the Crohn's disease. CT image-based diagnosis enables physicians to observe the entire intestines even if stenosed parts exist. CAD systems for Crohn's disease using CT volumes are recently developed. Such CAD systems need to reconstruct separated luminal regions of the intestines to analyze intestines. We propose a connection method of separated luminal regions of the intestines segmented from CT volumes. The luminal regions of the intestines are segmented from a CT volume. The centerlines of the luminal regions are calculated by using a thinning process. We enumerate all the possible sequences of the centerline segments. In this work, we newly introduce a condition using distance between connected ends points of the centerline segments. This condition eliminates unnatural connections of the centerline segments. Also, this condition reduces processing time. After generating a sequence list of the centerline segments, the correct sequence is obtained by using an evaluation function. We connect the luminal regions based on the correct sequence. Our experiments using four CT volumes showed that our method connected 6.5 out of 8.0 centerline segments per case. Processing times of the proposed method were reduced from the previous method.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Other approaches for lesion finding utilize motions of the intestines. 3,4 The intestines show different motion characteristics depending on lesion and nonlesion areas. Motions of the intestines are commonly obtained from time-series 2D MRI images.…”

Section: Introductionmentioning

confidence: 99%

Connection method of separated luminal regions of intestine from CT volumes

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Furukawa et al reviewed the effects of preparations, contrast agents, and scanning techniques for CT and MR image-based diagnosis [6]. Many researches use MR images for diagnosis [7]- [9]. Several reports related to CT image-based diagnosis have also been reported [8], [10]- [12].…”

Section: Introductionmentioning

confidence: 99%

Automated Ulcer Detection Method from CT Images for Computer Aided Diagnosis of Crohn's Disease

Oda

Kitasaka

Watanabe

et al. 2013

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYCrohn's disease commonly affects the small and large intestines. Its symptoms include ulcers and intestinal stenosis, and its diagnosis is currently performed using an endoscope. However, because the endoscope cannot pass through the stenosed parts of the intestines, diagnosis of the entire intestines is difficult. A CT image-based method is expected to become an alternative way for the diagnosis of Crohn's disease because it enables observation of the entire intestine even if stenosis exists. To achieve efficient CT image-based diagnosis, diagnostic-aid by computers is required. This paper presents an automated detection method of the surface of ulcers in the small and large intestines from fecal tagging CT images. Ulcers cause rough surfaces on the intestinal wall and consist of small convex and concave (CC) regions. We detect them by blob and inverse-blob structure enhancement filters. A roughness value is utilized to reduce the false positives of the detection results. Many CC regions are concentrated in ulcers. The roughness value evaluates the concentration ratio of the detected regions. Detected regions with low roughness values are removed by a thresholding process. The thickness of the intestinal lumen and the CT values of the surrounding tissue of the intestinal lumen are also used to reduce false positives. Experimental results using ten cases of CT images showed that our proposed method detects 70.6% of ulcers with 12.7 FPs/case. The proposed method detected most of the ulcers.

show abstract

“…MRI of the unprepared colon complicates automatic segmentation as the contrast between the gut tissue and surrounding tissue is significantly reduced compared to imaging using contrast agents or bowel preparation. As a consequence, publications on automatic colon segmentation from MRI are limited [14].…”

mentioning

confidence: 99%

A novel semi-automatic segmentation method for volumetric assessment of the colon based on magnetic resonance imaging

et al. 2015

View full text Add to dashboard Cite

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.

show abstract

A computer-assisted model for detection of MRI signs of Crohn’s disease activity: future or fiction?

Cited by 13 publications

References 33 publications

Connection method of separated luminal regions of intestine from CT volumes

Connection method of separated luminal regions of intestine from CT volumes

Automated Ulcer Detection Method from CT Images for Computer Aided Diagnosis of Crohn's Disease

A novel semi-automatic segmentation method for volumetric assessment of the colon based on magnetic resonance imaging

Contact Info

Product

Resources

About