Automatic detection of perforators for microsurgical reconstruction. (April 2020)

• Record Type: Journal Article
• Authors: Mavioso, Carlos; Araújo, Ricardo J.; Oliveira, Hélder P.; Anacleto, João C.; Vasconcelos, Maria Antónia; Pinto, David; Gouveia, Pedro F.; Alves, Celeste; Cardoso, Fátima; Cardoso, Jaime S.; Cardoso, Maria João
• Is Part Of: Breast. Volume 50(2020)
• Page Start: 19
• Page End: 24
• Publication Date: 2020-04
• Subjects: Microsurgery -- Perforators -- Pre-operative mapping -- DIEP -- Flap -- Automatic detection -- Computer vision -- Image analysis; Breast -- Diseases -- Periodicals; Breast -- Tumors -- Periodicals; Breast -- Periodicals; Electronic journals; Periodicals; 616
• DOI: 10.1016/j.breast.2020.01.001
• Languages: English
• ISSNs: 0960-9776

Abstract

Abstract: The deep inferior epigastric perforator (DIEP) is the most commonly used free flap in mastectomy reconstruction. Preoperative imaging techniques are routinely used to detect location, diameter and course of perforators, with direct intervention from the imaging team, who subsequently draw a chart that will help surgeons choosing the best vascular support for the reconstruction. In this work, the feasibility of using a computer software to support the preoperative planning of 40 patients proposed for breast reconstruction with a DIEP flap is evaluated for the first time. Blood vessel centreline extraction and local characterization algorithms are applied to identify perforators and compared with the manual mapping, aiming to reduce the time spent by the imaging team, as well as the inherent subjectivity to the task. Comparing with the measures taken during surgery, the software calibre estimates were worse for vessels smaller than 1.5 mm ( P = 6e-4) but better for the remaining ones ( P = 2e-3). Regarding vessel location, the vertical component of the software output was significantly different from the manual measure ( P = 0.02), nonetheless that was irrelevant during surgery as errors in the order of 2–3 mm do not have impact in the dissection step. Our trials support that a reduction of the time spent is achievable using the automatic tool (about 2 h/case). The introduction of artificial intelligence in clinical practice intends to simplify the work of health professionals and to provide better outcomes to patients. This pilot study paves the way for a success story. Graphical abstract: Image 1 Highlights: Automatic perforator detection was better compared to radiology team when estimating calibre of large vessels for DIEP flap. Software showed more difficulties when estimating the calibre of narrowest perforators. Time spent by radiology team evaluating AngioCT scans is reduced from about 2h per patient to 30 min when using the software.