Breast tumor segmentation in ultrasound using distance-adapted fuzzy connectedness, convolutional neural network, and active contour

This study addresses computer-aided breast cancer diagnosis through a hybrid framework for breast tumor segmentation in ultrasound images. The core of the three-stage method is based on the autoencoder convolutional neural network. In the first stage, we prepare a hybrid pseudo-color image through m...

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Vydáno v:Scientific reports Ročník 14; číslo 1; s. 25859 - 12
Hlavní autoři: Biesok, Marta, Juszczyk, Jan, Badura, Pawel
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 28.10.2024
Nature Publishing Group
Nature Portfolio
Témata:
631/67/1347
631/67/2321
639/166/985
692/700/1421/1860
Active contours
Algorithms
Breast cancer
Breast Neoplasms - diagnostic imaging
Breast Neoplasms - pathology
Breast tumor segmentation
Convolutional neural networks
Deep Learning
Female
Fuzzy connectedness
Fuzzy Logic
Humanities and Social Sciences
Humans
Image Interpretation, Computer-Assisted - methods
Image processing
Image Processing, Computer-Assisted - methods
multidisciplinary
Neural networks
Neural Networks, Computer
Science
Science (multidisciplinary)
Segmentation
Tumors
Ultrasonic imaging
Ultrasonography - methods
Ultrasonography, Mammary - methods
Ultrasound
Fuzzy connectedness
Breast cancer
Breast tumor segmentation
Convolutional neural networks
Ultrasound
Active contours
ISSN:2045-2322, 2045-2322
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Shrnutí:This study addresses computer-aided breast cancer diagnosis through a hybrid framework for breast tumor segmentation in ultrasound images. The core of the three-stage method is based on the autoencoder convolutional neural network. In the first stage, we prepare a hybrid pseudo-color image through multiple instances of fuzzy connectedness analysis with a novel distance-adapted fuzzy affinity. We produce different weight combinations to determine connectivity maps driven by particular image specifics. After the hybrid image is processed by the deep network, we adjust the segmentation outcome with the Chan-Vese active contour model. We find the idea of incorporating fuzzy connectedness into the input data preparation for deep-learning image analysis our main contribution to the study. The method is trained and validated using a combined dataset of 993 breast ultrasound images from three public collections frequently used in recent studies on breast tumor segmentation. The experiments address essential settings and hyperparameters of the method, e.g., the network architecture, input image size, and active contour setup. The tumor segmentation reaches a median Dice index of 0.86 (mean at 0.79) over the combined database. We refer our results to the most recent state-of-the-art from 2022–2023 using the same datasets, finding our model comparable in segmentation performance.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-76308-x