Related Experiment Video For Breast lesion
Updated: Jan 4, 2026
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
Published on: November 30, 2022
Evaluating the performance of convolutional neural networks with direct acyclic graph architectures in automatic
Marly Guimarães Fernandes Costa1, João Paulo Mendes Campos1, Gustavo de Aquino E Aquino1
1Centro de Tecnologia Eletrônica e da Informação/Universidade Federal do Amazonas, Av. General Rodrigo Otávio Jordão Ramos, 3000, Aleixo, Campus Universitário - Setor Norte, Pavilhão Ceteli, Manaus, AM, CEP: 69077-000, Brazil.
Background:
Outlining lesion contours in Ultra Sound (US) breast images is an important step in breast cancer diagnosis. Malignant lesions infiltrate the surrounding tissue, generating irregular contours, with spiculation and angulated margins, whereas benign lesions produce contours with a smooth outline and elliptical shape. In breast imaging, the majority of the existing publications in the literature focus on using Convolutional Neural Networks (CNNs) for segmentation and classification of lesions in mammographic images. In this study our main objective is to assess the ability of CNNs in detecting contour irregularities in breast lesions in US images.
Methods:
In this study we compare the performance of two CNNs with Direct Acyclic Graph (DAG) architecture and one CNN with a series architecture for breast lesion segmentation in US images. DAG and series architectures are both feedforward networks. The difference is that a DAG architecture could have more than one path between the first layer and end layer, whereas a series architecture has only one path from the beginning layer to the end layer. The CNN architectures were evaluated with two datasets.
Results:
With the more complex DAG architecture, the following mean values were obtained for the metrics used to evaluate the segmented contours: global accuracy: 0.956; IOU: 0.876; F measure: 68.77%; Dice coefficient: 0.892.
Conclusion:
The CNN DAG architecture shows the best metric values used for quantitatively evaluating the segmented contours compared with the gold-standard contours. The segmented contours obtained with this architecture also have more details and irregularities, like the gold-standard contours.
