Giuseppe Massimiani

Pooja P Jain, Pietro Mascagni, Giuseppe Massimiani et al.

Minimally invasive colorectal surgery is characterized by procedural variability, a difficult learning curve, and complications that impact quality and outcomes. Video-based assessment (VBA) offers an opportunity to generate data-driven insights to reduce variability, optimize training, and improve surgical performance. However, existing tools for workflow analysis remain difficult to standardize and implement. This study aims to develop and validate a VBA tool for workflow analysis across minimally invasive colorectal procedures. A Delphi process was conducted to achieve consensus on generalizable workflow descriptors. The resulting framework informed the development of a new VBA tool, ColoWorkflow. Independent raters then applied ColoWorkflow to a multicentre video dataset of laparoscopic and robotic colorectal surgery (CRS). Applicability and inter-rater reliability were evaluated. Consensus was achieved for 10 procedure-agnostic phases and 34 procedure-specific steps describing CRS workflows. ColoWorkflow was developed and applied to 54 colorectal operative videos (left and right hemicolectomies, sigmoid and rectosigmoid resections, and total proctocolectomies) from five centres. The tool demonstrated broad applicability, with all but one label utilized. Inter-rater reliability was moderate, with mean Cohen's K of 0.71 for phases and 0.66 for steps. Most discrepancies arose at phase transitions and step boundary definitions. ColoWorkflow is the first consensus-based, validated VBA tool for comprehensive workflow analysis in minimally invasive CRS. It establishes a reproducible framework for video-based performance assessment, enabling benchmarking across institutions and supporting the development of artificial intelligence-driven workflow recognition. Its adoption may standardize training, accelerate competency acquisition, and advance data-informed surgical quality improvement.

Adrien Meyer, Lorenzo Arboit, Giuseppe Massimiani et al.

Purpose: The Segment Anything Model (SAM) promises to ease the annotation bottleneck in medical segmentation, but overlapping anatomy and blurred boundaries make its point prompts ambiguous, leading to cycles of manual refinement to achieve precise masks. Better prompting strategies are needed. Methods: We propose a structured prompting strategy using 4 points as a compact instance-level shape description. We study two 4-point variants: extreme points and the proposed major/minor axis endpoints, inspired by ultrasound measurement practice. SAM cannot fully exploit such structured prompts because it treats all points identically and lacks geometry-aware reasoning. To address this, we introduce S4M (4-points to Segment Anything), which augments SAM to interpret 4 points as relational cues rather than isolated clicks. S4M expands the prompt space with role-specific embeddings and adds an auxiliary "Canvas" pretext task that sketches coarse masks directly from prompts, fostering geometry-aware reasoning. Results: Across eight datasets in ultrasound and surgical endoscopy, S4M improves segmentation by +3.42 mIoU over a strong SAM baseline at equal prompt budget. An annotation study with three clinicians further shows that major/minor prompts enable faster annotation. Conclusion: S4M increases performance, reduces annotation effort, and aligns prompting with clinical practice, enabling more scalable dataset development in medical imaging.