VOLUME ESTIMATION FROM SINGLE IMAGES: AN APPLICATION TO PANCREATIC ISLETS
DOI:
https://doi.org/10.5566/ias.1869Keywords:
Fakir probes, pancreatic islets, single image, volume estimation, 2D projectionAbstract
The present paper deals with the problem of volume estimation of individual objects from a single 2D view. Our main application is volume estimation of pancreatic (Langerhans) islets and the single 2D view constraint comes from the time and equipment limitations of the standard clinical procedure.
Two main approaches are followed in this paper. First, two regression-based methods are proposed, using a set of simple shape descriptors of the segmented image of the islet. Second, two example-based methods are proposed, based on a database of islets with known volume. For training and evaluation, islet volumes were determined by OPT microscopy and a semi-automatical stereological volume estimation using the so-called Fakir probes.
The performance of the single image volume estimation methods is studied on a set of 99 islets from human donors. Further experiments were also performed on a stone dataset and on synthetic 3D shapes, generated using a flexible stochastic particle model. The proposed methods are fast and the experimental results show that in most situations the proposed methods perform significantly better than the methods currently used in clinical practice, which are based on simple spherical or ellipsoidal models.
References
Alanentalo T et al. (2006). Tomographic molecular imaging and 3D quantification within adult mouse organs. Nat Methods 4:31–3.
Baddeley A, Jensen EBV (2005). Stereology for Statisticians. Boca Raton: Chapman & Hall/CRC.
Banta L et al. (2003). Estimation of limestone particle mass from 2D images. Powder Technol 132:184-9.
Berkova Z et al. (2005). Vitality of pancreatic islets labeled for magnetic resonance imaging with iron particles. Transplant Proc 37:3496-8.
Borovec J et al. (2017). Supervised and unsupervised segmentation using superpixels, model estimation, and Graph Cut. J. Electron. Imaging, 26:061610.
Buchwald P et al. (2016). Fully automated islet cell counter (ICC) for the assessment of islet mass, purity, and size distribution by digital image analysis. Cell Transplant 25:1747–61.
Chaarani B et al. (2013). Estimation of the lateral ventricles volumes from a 2D image and its relationship with cerebrospinal fluid flow. BioMed Research International 2013, Article ID 215989, 1-9.
Cramér H (1946). Mathematical Methods of Statistics. USA: Princeton University Press.
Feldkamp LA, Davis LC, Kress JW (1984). Practical cone-beam algorithm. J Opt Soc Am A 1:612-9.
Girman P et al. (2003). Digital imaging as a possible approach in evaluation of islet yield. Cell Transplant 12:129-33.
Habart D et al. (2016). Automated analysis of microscopic images of isolated pancreatic islets. Cell Transplant 25:2145-56.
Holm S (1979). A simple sequentially rejective multiple test procedure. Scand J Stat 6:65–70.
Howard CV, Reed MG (2005). Unbiased Stereology (second edition). Liverpool: QTP Publications.
Jensen EBV, Gundersen HJG (1993). The rotator. J Microsc 170:35-44.
Kubínová L, Janáček J (2001). Confocal microscopy and stereology: Estimating volume, number, surface area and length by virtual test probes applied to three-dimensional images. Microsc Res Tech 53:425–35.
Lehmann R et al. (2007). Superiority of small islets in human islet transplantation. Diabetes 56:594–603.
Niclauss N et al. (2008). Computer-assisted digital image analysis to quantify the mass and purity of isolated human islets before transplantation. Transplantation 86:1603-9.
Piccinini F et al. (2016). Single-image based methods used for non-invasive volume estimation of cancer spheroids: a practical assessing approach based on entry-level equipment. Comput Methods Programs Biomed 135:51–60.
Ricordi C et al. (1990). Islet isolation assessment in man and large animals. Acta Diabetol Lat 27:185–95.
Shapiro AM et al. (2000). Islet transplantation in seven patients with type 1 diabetes mellitus using a glucocorticoid-free immunosuppressive regimen. N Engl J Med 343:230–8.
Suszynski TM et al. (2014). Islet size index as a predictor of outcomes in clinical islet autotransplantation. Transplantation 97:1286–91.
Švihlík J et al. (2016). Automated separation of merged Langerhans islets. Medical Imaging 2016: Image Processing. SPIE Medical Imaging 2016. Bellingham: SPIE, 1-978438-7-978438.
Švihlík J et al. (2017). Langerhans islet volume estimation from 3D optical projection tomography. Lecture Notes in Computer Science (ACCV 2016) 10117:583-94.
Xu C et al. (2013). Image-Based Food Volume Estimation. In: CEA '13 Proceedings of the 5th international workshop on Multimedia for cooking & eating activities. New York: ACM, 75-80.
Yoo TS (2004). Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis. New York: CRC Press.
Zhang Z et al. (2011). Food volume estimation from a single image using virtual reality technology. In: 2011 IEEE 37th Annual Northeast Bioengineering Conference:1-2.
Ziegel JF, Nyengaard JR, Vedel Jensen EB (2015). Estimating particle shape and orientation using volume tensors. Scand J Stat 42:813-31.
Downloads
Published
Issue
Section
License
Copyright (c) 2018 Image Analysis & Stereology
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
