A novel approach to keypoint detection for the aesthetic evaluation of breast cancer surgery outcomes

Tiago Gonçalves, Wilson Silva, Maria J. Cardoso, Jaime S. Cardoso

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


The implementation of routine breast cancer screening and better treatment strategies made possible to offer to the majority of women the option of breast conservation instead of a mastectomy. The most important aim of breast cancer conservative treatment (BCCT) is to try to optimize aesthetic outcome and implicitly, quality of life (QoL) without jeopardizing local cancer control and overall survival. As a consequence of the impact aesthetic outcome has on QoL, there has been an effort to try to define an optimal tool capable of performing this type of evaluation. Starting from the classical subjective aesthetic evaluation of BCCT (either by the patient herself or by a group of clinicians through questionnaires) to an objective aesthetic evaluation (where machine learning and computer vision methods are employed), leads to less variability and increasing reproducibility of results. Currently, there are some offline software applications available such as BATⒸ and BCCT.core, which perform the assessment based on asymmetry measurements that are computed based on semi-automatically annotated keypoints. In the literature, one can find algorithms that attempt to do the completely automatic keypoint annotation with reasonable success. However, these algorithms are very time-consuming. As the course of research goes more and more into the development of web software applications, these time-consuming tasks are not desirable. In this work, we propose a novel approach to the keypoints detection task treating the problem in part as image segmentation. This novel approach can improve both execution-time and results.

Original languageEnglish
Article numberSI
Pages (from-to)891-903
JournalHealth and Technology
Issue number4
Publication statusPublished - Jul 2020


  • Aesthetic evaluation
  • Breast cancer
  • Deep neural networks
  • Keypoint detection


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