See the paper at https://www.scitepress.org/Link.aspx?doi=10.5220/0010258900600071 We conducted a user-centered design of a clinical decision-support system for breast cancer screening, diagnosis, and reporting based on stroke gestures. We combined knowledge elicitation interviews, scenario-focused questionnaires, and paper mock-ups to understand user needs. Multi-fidelity (low and high) prototypes were designed and compared first in vitro in a usability laboratory, then in vivo in the real world. The resulting user interface provides radiologists with a platform that integrates domain-oriented tools for the visualization of mammograms, the manual, and the semi-automatic annotation of breast cancer findings based on stroke gestures. The contribution of this work lies in that, to the best of our knowledge, stroke gestures have not yet been applied to the annotation of mammograms. On the one hand, although there is a substantial amount of research done in stroke-based interaction, none focuses especially on the domain of breast cancer annotation. On the other hand, typical gestures in breast cancer annotation tools are those with a keyboard and a mouse

1. User-Centered Development of a Clinical Decision-Support System for
Breast Cancer Diagnosis and Reporting based on Stroke Gestures
Suzanne Kieffer1, Annabelle Gouze 2 & Jean Vanderdonckt1
1 Université catholique de Louvain, Belgium
2 EVS Broadcast Equipment, Belgium

2. Support breast cancer screening and diagnosis
• Breast cancer (BC) is the most common cancer among women; its early
detection improves both treatment and recovery
• Advances in image processing allow the development of clinical decision
support system (CDSS) or computer-aided diagnosis (CAD) systems,
which help detect missed findings
• Highlighting lesions with markers such as crosses, triangles, or squares
• Discriminating findings into benign and malignant lesions

3. Problems with BC-CAD tools
• Radiologists deplore three main issues
1. They are not integrated in a unique system
2. They do not enable the standardized annotation of findings
3. They do not provide any means to interact with the CAD data
• BC-CAD data are incomplete
• Masses: segmented area, local contrast, perimeter and luminance
• Clusters of calcifications: center location, number of calcifications and density
• Microcalcifications: location, size, circularity, and local contrast
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4. Improve human labor during BC screening and diagnosis
• Support the screening exam workflow
• From image acquisition to reporting, including image screening, lesion detection,
lesion analysis and interpretation
• Report: complete and structured description of any significant finding, comparison to
previous studies, overall diagnosis, follow-up recommendations
• Use and improve image processing by integrating human knowledge
• Segmentation process: partitioning a digital image into multiple segments (sets of
pixels), which share certain characteristics such as color, intensity or texture
• Relevant feedback technique: incorporate user feedback about the relevance of the
results produced by the segmentation process
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5. Gesture-based interface
• Stroke gestures with a graphics tablet
and a pen as interaction modality
• Navigating among mammograms in a
clinical case
• Navigating in a specific case by zoom-in,
zoom-out and pan
• Sketching a region of interest (ROI)
• Annotating and reporting findings
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Wacom cintiq 21ux

6. Human-centered approach as development methodology
• User-centered design
• Better understand user needs and
expectations
• Increase system usefulness and
usability
• Increase user acceptance and
adoption
• Methods
• Knowledge elicitation interview
• Paper-and-pencil evaluation
• Formal usability testing
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7. Knowledge elicitation interviews  Screening workflow
1. Selection of a case
2. Analysis of patient information
3. Screening of views: global, cranio-
caudal (CC) projection, medio-lateral
oblique (MLO) projection, and global
again
4. In-depth screening of views to detect
suspicious findings
5. Interpretation of findings
6. Briefing and reporting of results
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8. Knowledge elicitation interviews  Need for a standardized
representation of BC characteristics
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9. Paper-and-pencil prototypes  Design and test ideas
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10. Paper-and-pencil prototypes
• Test-and-refine design ideas within a formative process
• Select and prioritize features worth implementing based on user-data
reduce development time and costs
• Collect additional domain- and task-relevant knowledge
• Description of the tasks sequence
• Issues regarding CB-CAD tools
• Needs and expectations
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11. Formal usability tests  Compare design solutions
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12. Radiologists prefer the star-menu over the pie-menu
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13. Relevance feedback: (a) Selection of a ROI ; (b) User
feedback ; (c) Improved segmentation (“smooth over”)
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14. Masses: predefined features, feedbacks, iconic feedback
representation and associated system actions
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15. Take-away
• User-centered development of a clinical decision-support system for
breast cancer diagnosis and reporting based on stroke gestures
• Contributions
• Improvement of the segmentation thanks to relevant feedback, allowing both
manual and semiautomatic diagnosis  Means to characterize any finding/lesion in
compliance with the approved standard of the domain
• Use of stroke gestures to annotate mammograms: research done in stroke-based
interaction does not focus on BC annotation ; typical gestures in BC annotation tools
are those with a keyboard and a mouse.
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16. Follow-up questions?
• Segmentation: annabelle.gouze@gmail.com
• User-centered approach: suzanne.kieffer@uclouvain.be
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