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Breast Tumor Tissue Segmentation with Area-Based Annotation Using Convolutional Neural Network.

Bendegúz H Zováthi1, Réka Mohácsi2, Attila Marcell Szász3

  • 1Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, 1083 Budapest, Hungary.

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|September 23, 2022
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Summary
This summary is machine-generated.

This study introduces an automated algorithm for precise breast cancer tissue segmentation, improving diagnostic speed and accuracy. The novel approach achieves a 99.10% f1 score, aiding pathologists in timely diagnoses.

Keywords:
breast cancercomputer-aided diagnosisconvolutional neural networksdeep learninghistopathological image segmentationmedical image classificationsliding window methodwhole slide image analysis

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Area of Science:

  • Digital pathology
  • Computational biology
  • Medical image analysis

Background:

  • Accurate histological diagnosis is crucial for effective breast cancer treatment (oncotherapy).
  • Current manual diagnostic processes are time-consuming and require specialized expertise, often delaying patient diagnosis.
  • Automated algorithms are needed to improve the efficiency and speed of breast cancer diagnostics.

Purpose of the Study:

  • To develop and validate a novel, automated algorithm for segmenting tumor and normal regions in high-resolution breast tissue images.
  • To provide an accurate and time-efficient alternative to manual histopathological analysis.
  • To assist pathologists in making timely and precise diagnostic decisions.

Main Methods:

  • An area-based annotation approach with a new rule template was developed for high-resolution biological segmentation.
  • A convolutional neural network was trained on a custom database of 291 breast tumor tissue images (70% for training).
  • The algorithm was evaluated on pixel-level segmentation using validation (10%) and test (20%) datasets, considering accuracy and time.

Main Results:

  • The automated algorithm achieved a 99.10% f1 score on pixel-level evaluation for histopathological image segmentation.
  • The system processed images efficiently, with an average segmentation time of 3 minutes.
  • Qualitative assessment by a histopathologist confirmed the algorithm's accuracy, even in previously unannotated regions.

Conclusions:

  • The proposed automated algorithm offers a highly accurate and time-efficient solution for breast cancer tissue segmentation.
  • This approach can significantly aid pathologists, potentially reducing diagnostic costs and improving patient outcomes.
  • The algorithm demonstrates the potential to enhance routine histopathological diagnostics through automated image analysis.