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<p>Standard plane acquisition in fetal neurosonography is essential for prenatal diagnosis, yet remains highly operator-dependent due to the need to identify subtle anatomical landmarks that define each imaging plane. Current automated approaches either treat the problem as plane-level image classification—without explicitly verifying landmark visibility—or employ computationally expensive object detection methods that may not be suitable for real-time clinical integration. To address these limitations, we propose a novel multitask learning framework that combines detection-based spatial awareness during training with efficient multilabel classification at inference. Validated on a public dataset collected at BCNatal center, the method was compared against a standard ResNet-101 multilabel baseline. Results demonstrate that the proposed framework achieves an average F1-score of 0.92, significantly outperforming the baseline, particularly for the challenging Cavum Septi Pellucidi where precision improves from 0.76 to 0.93. Grad-CAM visualizations reveal substantially more focused and anatomically precise attention maps compared to baseline approaches, with activations concentrated on clinically relevant substructures such as the cerebellar vermis. These findings confirm that, by discarding the auxiliary detection head at inference, the model retains the spatial features learned during training while minimizing computational overhead.</p>

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From classification to landmark-aware identification: a multi-task multilabel approach for fetal brain ultrasound planes

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月26日

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  • Maria Chiara Fiorentino,
  • Riccardo Rosati,
  • Edoardo Conti,
  • Costantino Tigano,
  • Adriano Mancini

摘要

Standard plane acquisition in fetal neurosonography is essential for prenatal diagnosis, yet remains highly operator-dependent due to the need to identify subtle anatomical landmarks that define each imaging plane. Current automated approaches either treat the problem as plane-level image classification—without explicitly verifying landmark visibility—or employ computationally expensive object detection methods that may not be suitable for real-time clinical integration. To address these limitations, we propose a novel multitask learning framework that combines detection-based spatial awareness during training with efficient multilabel classification at inference. Validated on a public dataset collected at BCNatal center, the method was compared against a standard ResNet-101 multilabel baseline. Results demonstrate that the proposed framework achieves an average F1-score of 0.92, significantly outperforming the baseline, particularly for the challenging Cavum Septi Pellucidi where precision improves from 0.76 to 0.93. Grad-CAM visualizations reveal substantially more focused and anatomically precise attention maps compared to baseline approaches, with activations concentrated on clinically relevant substructures such as the cerebellar vermis. These findings confirm that, by discarding the auxiliary detection head at inference, the model retains the spatial features learned during training while minimizing computational overhead.