An adaptive multi-scale network with nonorthogonal multi-union input for reducing false positive of lymph nodes

• Autorzy: Meng Xingqi , Peng Yanjun , Guo Yanfei

Identyfikatory

DOI

10.1016/j.bbe.2021.01.005

• Języki publikacji: EN

Abstrakty

EN

Lymph node (LN) detection and analysis are of great significance in terms of cancer staging and measuring the effectiveness of treatment. But, it is still a challenging and laborious task due to the lack of adequately-labeled data and the similar pathological features with surrounding structures in computed tomography (CT). In this paper, we propose a new representation for lymph node detection after augmentation, which can effectively decom-pose candidate cubic CT images by generating nonorthogonal multi-union 2D slices. These new views with coupling relationship will be used as a novel input to train the convolutional neural networks (CNNs) to achieve the purpose of reducing false positives (FP). In order to further adapt to the mutative radiological characteristics of lymph nodes, we designed an adaptive multi-scale network. This model adaptively learns the features of different scales images and redistributes weights of convolution kernel to optimize the classification result. We validate the approach on two datasets 90 CT volumes with 388 mediastinal LNs and 86 patients with 595 abdominal LNs. Our proposed methods perform better in both two cases, sensitivities reached 78%, 86% at 3 false-positives per patient volume (FP/vol.), and 94%, 96% at 6 FP/vol. in mediastinum and abdomen respectively.

Słowa kluczowe

EN

deep learning; lymph node detection; adaptive multi-scale network; false positive reduction

PL

uczenie głębokie; wykrywanie węzłów chłonnych; sieć wieloskalowa

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2021
• Tom: Vol. 41, no. 1
• Strony: 265--277
• Opis fizyczny: Bibliogr. 37 poz., rys., tab., wykr.

Twórcy

autor

Meng Xingqi

• College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China

autor

Peng Yanjun

• College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China; Shandong Province Key Laboratory of Wisdom Mining Information Technology, Shandong University of Science and Technology, Qingdao, China

autor

Guo Yanfei

• College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).