1. Time-money segment differences in ideation and collaboration readiness in sustainable tourism educationDejan Križaj, 2026, original scientific article Abstract: This study examines whether tourism students’ self-reported time–money use patterns are related to their readiness to collaborate on idea development, and whether sustain- ability emerges spontaneously in their tourism innovation ideas. Using an anonymised dataset of open-ended questionnaire responses from Slovenian higher education tourism students (N = 597; 2019–2025), we applied deterministic rule-based coding to classify the presence of actionable ideas and sustainability framing, as well as collaboration readiness and conditions. Actionable ideas were common (53.4%), but sustainability framing was uncommon (7.5%). Most respondents were unconditionally willing to collaborate (69.3%), while 30.7% expressed conditional willingness or unwillingness. Time–money behavioural segments were significantly associated with collaboration reservations, whereas segment differences in ideation and sustainability framing were not significant. Among students expressing reservations, topic match and perceived team quality were the most frequently stated conditions. These findings indicate that sustainability-oriented tourism education should support both sustainability integration and low-risk collaboration through clear project briefs, topic-based matching, and team-process supports. The conclusions should be interpreted with reasonable caution as they are context-specific evidence based on self- reported, rule-coded responses, particularly for sustainability framing, where positive cases were rare. In this context, segmentation should be regarded as a diagnostic tool for course design rather than as a basis for labelling students.
Keywords: tourism education, sustainability, collaboration readiness, behavioural segmentation, time–money trade-offs, project-based learning, open-ended survey, clustering
Published in RUP: 04.05.2026; Views: 243; Downloads: 14
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2. Deep learning for brain MRI tissue and structure segmentation : a comprehensive reviewNedim Šišić, Peter Rogelj, 2025, review article Abstract: Brain MRI segmentation plays a crucial role in neuroimaging studies and clinical trials by enabling the precise localization and quantification of brain tissues and structures. The advent of deep learning has transformed the field, offering accurate and fast tools for MRI segmentation. Nevertheless, several challenges limit the widespread applicability of these methods in practice. In this systematic review, we provide a comprehensive analysis of developments in deep learning-based segmentation of brain MRI in adults, segmenting the brain into tissues, structures, and regions of interest. We explore the key model factors influencing segmentation performance, including architectural design, choice of input size and model dimensionality, and generalization strategies. Furthermore, we address validation practices, which are particularly important given the scarcity of manual annotations, and identify the limitations of current methodologies. We present an extensive compilation of existing segmentation works and highlight the emerging trends and key results. Finally, we discuss the challenges and potential future directions in the field.
Keywords: magnetic resonance imaging, brain, image segmentation, deep learning
Published in RUP: 10.10.2025; Views: 1104; Downloads: 17
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4. Kidney segmentation in renal magnetic resonance imaging - current status and prospectsFrank G. Zöllner, Marek Kociński, Laura L. Hansen, Alena-Kathrin Golla, Amira Šerifović-Trbalić, A. Lundervold, Andrzej Materka, Peter Rogelj, 2021, review article Keywords: renal MRI, image segmentation, deep learning
Published in RUP: 04.06.2021; Views: 3735; Downloads: 51
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6. Levelling the playing field : the effects of Slovenia's 2013 labour market reformMatija Vodopivec, Suzana Laporšek, Milan Vodopivec, 2016, treatise, preliminary study, study Keywords: varovanje zaposlitve, dinamika trga dela, segmentacija trga dela, employment protection legislation, labor market dynamics, labor market segmentation
Published in RUP: 08.08.2016; Views: 5931; Downloads: 94
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7. Ellipsoid segmentation model for analyzing light-attenuated 3D confocal image stacks of fluorescent multi-cellular spheroidsMichaël Barbier, Steffen Jaensch, Frans Cornelissen, Suzana Vidic, Kjersti Gjerde, Ronald de Hoogt, Ralph Graeser, Emmanuel Gustin, Yolanda T. Chong, 2016, original scientific article Keywords: 3D models, segmentation, confocal microscope, image analysis, fluorescence
Published in RUP: 08.08.2016; Views: 4798; Downloads: 181
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