A unified 2D medical image segmentation network (SegmentNet) through distance-awareness and local feature extraction

• Autorzy: Ejiyi Chukwuebuka Joseph , Qin Zhen , Ukwuoma Chiagoziem , Agbesi Victor Kwaku , Oluwasanmi Ariyo , Al-antari Mugahed A. , Bamisile Olusola

Identyfikatory

DOI

10.1016/j.bbe.2024.06.001

• Języki publikacji: EN

Abstrakty

EN

In addressing the challenges of medical image segmentation, particularly the elusiveness of global context and limitations in leveraging both global and local context simultaneously, we present SegmentNet as a solution. Our approach involves a step-by-step implementation within the reconstructed UNet architecture, tailored to enhance segmentation performance across diverse medical imaging modalities. The first step involves the integration of multi-focus Distance-Aware Mechanisms (DaMs) within skip connections and between successive layers of the encoder in SegmentNet. This strategic placement focuses on extracting unrelated features, ensuring comprehensive consideration of global context. Following this, Local Feature Extractor Blocks (LFEBs) are introduced at the base of the network. Equipped with depthwise separable operations, standard convolutions, smoothed ReLU, and normalization transform, LFEBs target the capture of specific local image features ensuring that features overlooked by DaMs are appropriately considered. These extracted features are then passed on to the decoder portion of SegmentNet, facilitating enhanced prediction of masks thus, optimizing segmentation performance. Evaluated across diverse datasets, including Breast Ultrasound Images (BUSI), Chest X-ray images (CXRI), and Diabetic Retinal Fundus Images (DRFI), SegmentNet excels. The segmentation evaluation results in terms of accuracy, Jaccard, and specificity are respectively recorded for BUSI, CXRI, and DRFI to be (93.88 %, 98.96 %, and 99.17 %), (99.28 %, 99.58 %, and 99.83 %), and (95.77 %, 95.95 %, and 99.94 %). Thus, showing that the incorporation of DaMs and LFEBs in SegmentNet emerges as a robust solution demonstrating precise 2D medical image segmentation across various modalities. This advancement holds significant potential for diverse clinical applications, promising improved patient care.

Słowa kluczowe

EN

medical image segmentation; distance aware mechanisms; DaMs; local feature extractor blocks; LFEBs

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2024
• Tom: Vol. 44, no. 3
• Strony: 431--449
• Opis fizyczny: Bibliogr. 88 poz., rys., tab., wykr.

Twórcy

autor

Ejiyi Chukwuebuka Joseph

• Network and Data Security Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China

autor

Qin Zhen

• Network and Data Security Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China

autor

Ukwuoma Chiagoziem

• Sichuan Engineering Technology Research Center for Industrial Internet Intelligent Monitoring and Application, Chengdu University of Technology, Sichuan, China

autor

Agbesi Victor Kwaku

• School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

autor

Oluwasanmi Ariyo

• Network and Data Security Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China

autor

Al-antari Mugahed A.

• Department of Artificial Intelligence, College of Software & Convergence Technology, Daeyang AI Center, Sejong University, Seoul, Republic of Korea

autor

Bamisile Olusola

• Sichuan Engineering Technology Research Center for Industrial Internet Intelligent Monitoring and Application, Chengdu University of Technology, Sichuan, China

Bibliografia

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