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<p>Parasitic infections pose a significant health risk in many regions worldwide, requiring rapid and reliable diagnostic methods to identify and treat affected individuals. Recent advancements in deep learning have significantly improved the accuracy and efficiency of microscopic image analysis workflows, enabling its application in various domains such as medical diagnostics and microbiology. This work presents DT4PEIS, a novel two-stage architecture for the instance segmentation of parasite eggs in microscopic images. The first stage is a DEtection TRansformer (DETR) based architecture, which predicts the bounding boxes and class labels of the detected eggs. Then, the predicted bounding boxes are used as prompts to guide the segmentation process in the second stage, which is based on the Segment Anything Model (SAM) architecture. We evaluate the performance of the proposed method on the Chula-ParasiteEgg-11 dataset. Our results show that the proposed method outperforms the other architectures in terms of segmentation <i>mean Average Precision</i> (<i>mAP</i>), providing a more detailed and accurate representation of the detected eggs.</p>

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DT4PEIS: detection transformers for parasitic egg instance segmentation

  • 期刊论文

  • 发表日期：

    2025年1月4日

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  • Jesus Ruiz-Santaquiteria,
  • Anibal Pedraza,
  • Oscar Deniz,
  • Gloria Bueno

摘要

Parasitic infections pose a significant health risk in many regions worldwide, requiring rapid and reliable diagnostic methods to identify and treat affected individuals. Recent advancements in deep learning have significantly improved the accuracy and efficiency of microscopic image analysis workflows, enabling its application in various domains such as medical diagnostics and microbiology. This work presents DT4PEIS, a novel two-stage architecture for the instance segmentation of parasite eggs in microscopic images. The first stage is a DEtection TRansformer (DETR) based architecture, which predicts the bounding boxes and class labels of the detected eggs. Then, the predicted bounding boxes are used as prompts to guide the segmentation process in the second stage, which is based on the Segment Anything Model (SAM) architecture. We evaluate the performance of the proposed method on the Chula-ParasiteEgg-11 dataset. Our results show that the proposed method outperforms the other architectures in terms of segmentation mean Average Precision (mAP), providing a more detailed and accurate representation of the detected eggs.