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Deep Inferior Epigastric Perforator (DIEP) flap breast reconstruction relies on the precise identification of perforator vessels supplying blood to transferred tissue. Traditional manual mapping from preoperative imaging is time-consuming and subjective. To address this, AVA, a semi-automated perforator detection algorithm, was developed to analyze angiography images. AVA follows a three-step process: automated anatomical segmentation, manual annotation of perforators, and segmentation of perforator courses. This approach enhances accuracy, reduces subjectivity, and accelerates the mapping process while generating quantitative reports for surgical planning. To streamline integration into clinical workflows, AVA has been embedded into PACScenter, a medical imaging platform, leveraging DICOM encapsulation for seamless data exchange within a Vendor Neutral Archive (VNA). This integration allows surgeons to interactively annotate perforators, adjust parameters iteratively, and visualize detailed anatomical structures. AVA-PACScenter integration eliminates workflow disruptions by providing real-time perforator analysis within the surgical environment, ultimately improving preoperative planning and intraoperative guidance. Currently undergoing clinical feasibility testing, this integration aims to enhance DIEP flap reconstruction efficiency by reducing manual inputs, improving mapping precision, and facilitating long-term report storage within Dicoogle. By automating perforator analysis, AVA represents a significant advancement toward data-driven, patient-centered surgical planning.

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Integrating Automated Perforator Analysis for Breast Reconstruction in Medical Imaging Workflow

  • 会议论文

  • 发表日期：

    2025年

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  • José Frias,
  • Miguel Romariz,
  • Ricardo Ferreira,
  • Tânia Pereira,
  • Hélder P. Oliveira,
  • João Santinha,
  • David Pinto,
  • Pedro Gouveia,
  • Luís Bastião Silva,
  • Carlos Costa

摘要

Deep Inferior Epigastric Perforator (DIEP) flap breast reconstruction relies on the precise identification of perforator vessels supplying blood to transferred tissue. Traditional manual mapping from preoperative imaging is time-consuming and subjective. To address this, AVA, a semi-automated perforator detection algorithm, was developed to analyze angiography images. AVA follows a three-step process: automated anatomical segmentation, manual annotation of perforators, and segmentation of perforator courses. This approach enhances accuracy, reduces subjectivity, and accelerates the mapping process while generating quantitative reports for surgical planning. To streamline integration into clinical workflows, AVA has been embedded into PACScenter, a medical imaging platform, leveraging DICOM encapsulation for seamless data exchange within a Vendor Neutral Archive (VNA). This integration allows surgeons to interactively annotate perforators, adjust parameters iteratively, and visualize detailed anatomical structures. AVA-PACScenter integration eliminates workflow disruptions by providing real-time perforator analysis within the surgical environment, ultimately improving preoperative planning and intraoperative guidance. Currently undergoing clinical feasibility testing, this integration aims to enhance DIEP flap reconstruction efficiency by reducing manual inputs, improving mapping precision, and facilitating long-term report storage within Dicoogle. By automating perforator analysis, AVA represents a significant advancement toward data-driven, patient-centered surgical planning.