Multi-stage fully convolutional network for precise prostate segmentation in ultrasound images

• Autorzy: Feng Yujie , Atabansi Chukwuemeka Clinton , Nie Jing , Liu Haijun , Zhou Hang , Zhao Huai , Hong Ruixia , Li Fang , Zhou Xichuan

Identyfikatory

DOI

10.1016/j.bbe.2023.08.002

• Języki publikacji: EN

Abstrakty

EN

Prostate cancer is one of the most commonly diagnosed non-cutaneous malignant tumors and the sixth major cause of cancer-related death generally found in men globally. Automatic segmentation of prostate regions has a wide range of applications in prostate cancer diagnosis and treatment. It is challenging to extract powerful spatial features for precise prostate segmentation methods due to the wide variation in prostate size, shape, and histopathologic heterogeneity among patients. Most of the existing CNN-based architectures often produce unsatisfactory results and inaccurate boundaries in prostate segmentation, which are caused by inadequate discriminative feature maps and the limited amount of spatial information. To address these issues, we propose a novel deep learning technique called Multi-Stage FCN architecture for 2D prostate segmentation that captures more precise spatial information and accurate prostate boundaries. In addition, a new prostate ultrasound image dataset known as CCH-TRUSPS was collected from Chongqing University Cancer Hospital, including prostate ultrasound images of various prostate cancer architectures. We evaluate our method on the CCH-TRUSPS dataset and the publicly available Multi-site T2-weighted MRI dataset using five commonly used metrics for medical image analysis. When compared to other CNN-based methods on the CCH-TRUSPS test set, our Multi-Stage FCN achieves the highest and best binary accuracy of 99.15%, the DSC score of 94.90%, the IoU score of 89.80%, the precision of 94.67%, and the recall of 96.49%. The statistical and visual results demonstrate that our approach outperforms previous CNN-based techniques in all ramifications and can be used for the clinical diagnosis of prostate cancer.

Słowa kluczowe

EN

deep learning; prostate segmentation; ultrasound image; prostate cancer diagnosis

PL

uczenie głębokie; segmentacja prostaty; obraz ultrasonograficzny; nowotwór prostaty

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2023
• Tom: Vol. 43, no. 3
• Strony: 586--602
• Opis fizyczny: Bibliogr. 79 poz., rys., tab.

Twórcy

autor

Feng Yujie

• Department of Ultrasound, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing University Cancer Hospital, Chongqing, China

autor

Atabansi Chukwuemeka Clinton

• School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China

autor

Nie Jing

• School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China

autor

Liu Haijun

• School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China

autor

Zhou Hang

• Department of Ultrasound, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing University Cancer Hospital, Chongqing, China

autor

Zhao Huai

• Department of Ultrasound, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing University Cancer Hospital, Chongqing, China

autor

Hong Ruixia

• Department of Ultrasound, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing University Cancer Hospital, Chongqing, China

autor

Li Fang

• Department of Ultrasound, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), Chongqing University 11 Cancer Hospital, Chongqing 400030, China

autor

Zhou Xichuan

• School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China

Bibliografia

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Poprawka do artykułu znajduje się w : Biocybernetics and Biomedical Engineering , 2023, Vol. 43, no 4, s. 776