VALIDATION / REFER TO SEGMENT

Here you find description of how to proper refer to Segment in your publications

Validation is the key for getting robust and trustworthy measurements. Close collaboration with the top researchers in the field makes our analysis methods continually updated and state of the art.

Proper referencing of Segment when publishing scientific results is a prerequisite for using Segment. This is vital. We are dependent on proper citations in order to continue to release the software freely available for researchers.

Please send full bibliographic information (such as the Pubmed link) of your final work, when published or accepted for publication, to support@medviso.com. Please see the list of researchers who has already remembered to give us credit by a proper citation in our publications list.

How to refer to Segment

A reference should encompass both the name Segment, and a suitable publication. When in doubt, please send an email to support@medviso.com or put reference [1] which is the generic reference for image analysis in Segment. This open-access paper describes Segment and its potential uses. 

Examples of possible formulations for references:

  • All image analysis was done using the freely available software Segment v4.0 RXXXXX (Medviso, Lund, Sweden) [1].
  • Global LV function was quantified using Segment v4.0 RXXXXX (Medviso, Lund, Sweden) [2].
  • Infarct size was quantified using Segment v4.0 RXXXXX (Medviso, Lund, Sweden) [6].

Note that referencing the software is mandatory also for abstracts to scientific conferences. If shortage of space, at least reference the software as something like:
Images were analysed using Segment (Medviso).

In extreme shortage of space, such as conferences where the word limit is less than 350 words, then reference may be omitted in the abstract text, but should be included in the oral presentation and / or poster.

References

General software reference

[1] E. Heiberg, J. Sjögren, M. Ugander, M. Carlsson, H. Engblom, and H. Arheden, Design and Validation of Segment – a Freely Available Software for Cardiovascular Image Analysis, BMC Medical Imaging, 10:1, 2010.

LV segmentation

Using the latest AI-based LV segmentation in the software (version later than 3.1 R8109) should be referenced by [2]. Using the semi-automated LV segmentation in the software (version v2.0 R4265 – v3.0 R8052) should be referenced by [3]. Using the alternative semi-automatic LV segmentation in the software (version earlier than v1.9 R4245) should be referenced by [4]. Using the software for manual segmentation of the LV should be referenced by [1].

[2] K. Berggren, E. Hedstrom, K. Steding Ehrenborg, M. Carlsson, H. Engblom,  E. Ostenfeld,  J. Jogi,  D. Atar,  U. Ekelund,  H. Arheden,  E.Heiberg, Multiple Convolutional Neural Networks for Robust Myocar-dial Segmentation. In proceedings of SSBA 2020

[3] J. Tufvesson, E. Hedstrom, K. Steding-Ehrenborg, M. Carlsson, H. Arheden, and E. Heiberg, Validation and Development of a New Automatic Algorithm for Time-Resolved Segmentation of the Left Ventricle in Magnetic Resonance Imaging, Biomed Res Int, 2015:970357.

[4] E. Heiberg, L. Wigström, M. Carlsson, A.F. Bolger, M. Karlsson, Time Resolved Three-dimensional Segmentation of the Left Ventricle, In proceedings of IEEE Computers In Cardiology 2005(32), pp. 599-602, Lyon, France, 2005.

RV segmentation

Using the latest AI-based RV segmentation in the software (version later than 4.0 R11044) should be referenced by [5].

[5] J. Åkesson, E. Ostenfeld, M. Carlsson, H. Arheden, and E. Heiberg, Deep learning can yield clinically useful right ventricular segmentations faster than fully manual analysis, Sci. Rep., vol. 13:1216, pp. 1–10, 2023.

Infarct quantification

The current algorithm for infarct quantification is EWA and should be referenced as [6]. The old weighted version should be referenced as [7]. Measurement of endocardial extent should be referenced to as [8]. Gray zone analysis should be referenced as gray zone analysis using weighted method using either [6] or [7] as reference. If the ROI based gray zone algorithm is used then the algorithm should be referred to as [9].

[6] H. Engblom, J. Tufvesson, R. Jablonowski, M. Carlsson, A. H. Aletras, P. Hoffmann, A. Jacquier, F. Kober, B. Metzler, D. Erlinge, D. Atar, H. Arheden, and E. Heiberg, A new automatic algorithm for quantification of myocardial infarction imaged by late gadolinium enhancement cardiovascular magnetic resonance: experimental validation and comparison to expert delineations in multi-center, multi-vendor patient data, J Cardiovasc Magn Reson 18(1) p 27, 2016.

[7] E. Heiberg, M. Ugander, H. Engblom, M. Götberg, G. K. Olivecrona, D. Erlinge, and H. Arheden, Automated quantification of myocardial infarction from MR images by accounting for partial volume effects: animal, phantom, and human study, Radiology 246(2) pp. 581-8, 2008.

[8] H. Engblom, M. B. Carlsson, E. Hedstrom, E. Heiberg, M. Ugander, G. S. Wagner, and H. Arheden, The endocardial extent of reperfused first-time myocardial infarction is more predictive of pathologic Q waves than is infarct transmurality: a magnetic resonance imaging study, Clin Physiol Funct Imaging 27(2) pp. 101-8, 2007.

[9] Wu KC, Gerstenblith G, Guallar E, Marine JE, Dalal D, Cheng A, Marbán E, Lima JAC, Tomaselli GF, Weiss RG. Combined cardiac MRI and C-reactive protein levels identify a cohort at low risk for defibrillator firings and death. Circ Cardiovasc Imaging 2012; 5:178-86. PMCID:PMC3330427

Flow quantification

[10] Bidhult S, Hedström E, Carlsson M, Töger J, Steding-Ehrenborg K, Arheden H, Aletras AH, Heiberg E.
A new vessel segmentation algorithm for robust blood flow quantification from two-dimensional phase-contrast magnetic resonance images. Clin Physiol Funct Imaging. 2019 Sep;39(5):327-338.

Strain analysis module

The underlying algorithm for the new Strain MITT module is given in, and should be referenced as, [11]. The underlying algorithm for the first generation Strain module is given in, and should be referenced as, [12] or [13]. The clinical reproducibility of feature tracking in cine MRI is reported in [14]. 

[11] S. Queiros, P. Morais, D. Barbosa, J. C. Fonseca, J. L. Vilaca, and J. D’Hooge, “MITT: Medical Image Tracking Toolbox,” IEEE Trans. Med. Imaging, vol. 37, no. 11, pp. 2547–2557, 2018, doi: 10.1109/TMI.2018.2840820.

[12] B. Heyde, R. Jasaityte, D. Barbosa, V. Robesyn, S. Bouchez, P. Wouters, F. Maes, P. Claus, J. D’hooge. Elastic image registration versus speckle tracking for 2-D myocardial motion estimation: a direct comparison in vivo. IEEE Trans Med Imaging. 2013 Feb;32(2):449-459.

[13] P. Morais, B. Heyde, D. Barbosa, S. Queirós, P. Claus, and J. D’hooge. Cardiac motion and deformation estimation from tagged MRI sequences using a temporal coherent image registration framework. Proceedings of the meeting on Functional Imaging and Modelling of the Heart (FIMH), Lecture Notes in Computer Science, vol. 7945, pages 316-324, London, 2013.

[14] P. Morais, A. Marchi, JA. Bogaert, T. Dresselaers, B. Heyde, J. D’hooge, J. Bogaert. Cardiovascular magnetic resonance myocardial feature tracking using a non-rigid, elastic image registration algorithm: assessment of variability in a real-life clinical setting. J Cardiovasc Magn Reson 2017 Feb 17;19(1):24.

Myocardium at Risk

Segmentation of myocardium at risk from T2 STIR imaging should be referred to as [15]. Segmentation of myocardium at risk from CE-SSFP should be referenced to as [16].

[15] J. Sjogren, J. F. Ubachs, H. Engblom, M. Carlsson, H. Arheden, and E. Heiberg, Semi-automatic segmentation of myocardium at risk in T2-weighted cardiovascular magnetic resonance, J Cardiovasc Magn Reson 14(1) p 10, 2012.

[16] J. Tufvesson, M. Carlsson, A. H. Aletras, H. Engblom, J. F. Deux, S. Koul, P. Sorensson, J. Pernow, D. Atar, D. Erlinge, H. Arheden, and E. Heiberg, Automatic segmentation of myocardium at risk from contrast enhanced SSFP CMR: validation against expert readers and SPECT, BMC Med Imaging 16 p 19, 2016.

Bulls eye analysis

Creating and exporting bulls eyes plots should be referenced to as [17].

[17]P. A. Cain, M. Ugander, J. Palmer, M. Carlsson, E. Heiberg, and H. Arheden, Quantitative polar representation of left ventricular myocardial perfusion, function and viability using SPECT and cardiac magnetic resonance: initial results, Clin Physiol Funct Imaging 25(4) pp. 215-22, 2005

Mapping analysis

Creating T2* maps and analysis should be referenced to as [18]. Creating T1, or T2 maps and analysis should be referenced as [19].

[18] S. Bidhult, C. G. Xanthis, L. L. Liljekvist, G. Greil, E. Nagel, A. H. Aletras, E. Heiberg, E. Hedström, Validation of a New T2* Algorithm and Its Uncertainty Value for Cardiac and Liver Iron Load Determination from MRI Magnitude Images. Magn Reson Med, May 22, 2015.

[19] S. Bidhult, G. Kantasis, A. H. Aletras, H. Arheden, E. Heiberg, and E. Hedstrom, Validation of T1 and T2 algorithms for quantitative MRI: performance by a vendor-independent software, BMC Med Imaging 16(1) p 46, 2016.

Pulse wave velocity

Usage of the pulse wave velocity should be referenced as [20].

[20] Lundström S, Liefke J, Heiberg E, Hedström E. Pulse Wave Velocity Measurements by Magnetic Resonance Imaging in Neonates and Adolescents: Methodological Aspects and Their Clinical Implications. Pediatr Cardiol. 2022 Apr 9. doi: 10.1007/s00246-022-02894-0. Epub ahead of print. PMID: 35396945.

Fusion module

Usage of the Fusion Module should be referenced with reference [21].

[21] M. Ugander, H. Soneson, H. Engblom, J. v. d. Pals, D. Erlinge, E. Heiberg, and H. Arheden, Semi-automatic quantification of myocardium at risk in myocardial perfusion SPECT by co-registration and fusion with delayed contrast enhanced MR imaging – an experimental ex vivo study, Clin Physiol Func Imag, 32(1) pp 33-38, 2012.

Myocardial perfusion SPECT analysis

Quantification of LV mass in SPECT images should be referenced to with reference [22]. Quantification myocardium at risk in SPECT images should be referenced to with reference [23]. Quantification of LV volumes in SPECT images should be referenced to with reference [24]. Quantification of ischemia in SPECT images should be referenced to with reference [25].

[22] H. Soneson, J. F. Ubachs, M. Ugander, H. Arheden, and E. Heiberg, An Improved Method for Automatic Segmentation of the Left Ventricle in Myocardial Perfusion SPECT, J Nucl Med 50(2) pp. 205-13, 2009.

[23] H. Soneson, H. Engblom, E. Hedstrom, F. Bouvier, P. Sorensson, J. Pernow, H. Arheden, and E. Heiberg, An automatic method for quantification of myocardium at risk from myocardial perfusion SPECT in patients with acute coronary occlusion, J Nucl Cardiol 17(5) pp. 831-40, 2010.

[24] H. Soneson, F. Hedeer, C. Arevalo, M. Carlsson, H. Engblom, J. F. Ubachs, H. Arheden, and E. Heiberg, Development and validation of a new automatic algorithm for quantification of left ventricular volumes and function in gated myocardial perfusion SPECT using cardiac magnetic resonance as reference standard, J Nucl Cardiol, 2011.

[25] H. Fransson, M. Ljungberg, M. Carlsson, H. Engblom, H. Arheden, and E. Heiberg, Validation of an automated method to quantify stress-induced ischemia and infarction in rest-stress myocardial perfusion SPECT, J Nucl Cardiol, 21(3) pp 503-18 2014.