Adrien Meyer

Adrien Meyer, Didier Mutter, Nicolas Padoy

Detecting anatomical landmarks in medical imaging is essential for diagnosis and intervention guidance. However, object detection models rely on costly bounding box annotations, limiting scalability. Weakly Semi-Supervised Object Detection (WSSOD) with point annotations proposes annotating each instance with a single point, minimizing annotation time while preserving localization signals. A Point-to-Box teacher model, trained on a small box-labeled subset, converts these point annotations into pseudo-box labels to train a student detector. Yet, medical imagery presents unique challenges, including overlapping anatomy, variable object sizes, and elusive structures, which hinder accurate bounding box inference. To overcome these challenges, we introduce DExTeR (DETR with Experts), a transformer-based Point-to-Box regressor tailored for medical imaging. Built upon Point-DETR, DExTeR encodes single-point annotations as object queries, refining feature extraction with the proposed class-guided deformable attention, which guides attention sampling using point coordinates and class labels to capture class-specific characteristics. To improve discrimination in complex structures, it introduces CLICK-MoE (CLass, Instance, and Common Knowledge Mixture of Experts), decoupling class and instance representations to reduce confusion among adjacent or overlapping instances. Finally, we implement a multi-point training strategy which promotes prediction consistency across different point placements, improving robustness to annotation variability. DExTeR achieves state-of-the-art performance across three datasets spanning different medical domains (endoscopy, chest X-rays, and endoscopic ultrasound) highlighting its potential to reduce annotation costs while maintaining high detection accuracy.

Adrien Meyer, Aditya Murali, Farahdiba Zarin et al.

Purpose: Automated ultrasound image analysis is challenging due to anatomical complexity and limited annotated data. To tackle this, we take a data-centric approach, assembling the largest public ultrasound segmentation dataset and training a versatile visual foundation model tailored for ultrasound. Methods: We compile US-43d, a large-scale collection of 43 open-access ultrasound datasets with over 280,000 images and segmentation masks for more than 50 anatomical structures. We then introduce UltraSam, an adaptation of the Segment Anything Model (SAM) that is trained on US-43d and supports both point- and box-prompts. Finally, we introduce a new use case for SAM-style models by using UltraSam as a model initialization that can be fine-tuned for various downstream analysis tasks, demonstrating UltraSam's foundational capabilities. Results: UltraSam achieves vastly improved performance over existing SAM-style models for prompt-based segmentation on three diverse public datasets. Moreover, an UltraSam-initialized Vision Transformer surpasses ImageNet-, SAM-, and MedSAM-initialized models in various downstream segmentation and classification tasks, highlighting UltraSam's effectiveness as a foundation model. Conclusion: We compile US-43d, a large-scale unified ultrasound dataset, and introduce UltraSam, a powerful multi-purpose SAM-style model for ultrasound images. We release our code and pretrained models at https://github.com/CAMMA-public/UltraSam and invite the community to further this effort by contributing high-quality datasets.

Meyer Adrien, Mazellier Jean-Paul, Jeremy Dana et al.

Purpose: In medical research, deep learning models rely on high-quality annotated data, a process often laborious and timeconsuming. This is particularly true for detection tasks where bounding box annotations are required. The need to adjust two corners makes the process inherently frame-by-frame. Given the scarcity of experts' time, efficient annotation methods suitable for clinicians are needed. Methods: We propose an on-the-fly method for live video annotation to enhance the annotation efficiency. In this approach, a continuous single-point annotation is maintained by keeping the cursor on the object in a live video, mitigating the need for tedious pausing and repetitive navigation inherent in traditional annotation methods. This novel annotation paradigm inherits the point annotation's ability to generate pseudo-labels using a point-to-box teacher model. We empirically evaluate this approach by developing a dataset and comparing on-the-fly annotation time against traditional annotation method. Results: Using our method, annotation speed was 3.2x faster than the traditional annotation technique. We achieved a mean improvement of 6.51 +- 0.98 AP@50 over conventional method at equivalent annotation budgets on the developed dataset. Conclusion: Without bells and whistles, our approach offers a significant speed-up in annotation tasks. It can be easily implemented on any annotation platform to accelerate the integration of deep learning in video-based medical research.