A Neural Network for Segmenting Tumours in Ultrasound Rectal Images.
Ultrasound imaging is the most cost-effective approach for the early detection of rectal cancer, which is a high-risk cancer. Our goal was to design an effective method that can accurately identify and segment rectal tumours in ultrasound images, thereby facilitating rectal cancer diagnoses for phys...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 4; pp. 2229 - 2241 |
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| Autores principales: | , , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187278985&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187278985 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Aug2025 vid: 38 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187278985 187278985 187278985 10.1007/s10278-024-01358-6 187278985 ppf: 2229 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Neural Network for Segmenting Tumours in Ultrasound Rectal Images. aug: au: Zhang, Yuanxi Deng, Xiwen Li, Tingting Li, Yuan Wang, Xiaohui Lu, Man Yang, Lifeng affil: https://ror.org/04qr3zq92 School of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, People's Republic of China sug: subj: Image Processing, Computer Assisted Rectal Neoplasms Ultrasonography Neural Networks (Computer) Utilization Neoplasms Diagnosis Diagnostic Imaging Methods Funding Source Human Hospitals China Deep Learning Methods Descriptive Statistics Paired T-Tests Post Hoc Analysis Comparative Studies Validation Studies ab: Ultrasound imaging is the most cost-effective approach for the early detection of rectal cancer, which is a high-risk cancer. Our goal was to design an effective method that can accurately identify and segment rectal tumours in ultrasound images, thereby facilitating rectal cancer diagnoses for physicians. This would allow physicians to devote more time to determining whether the tumour is benign or malignant and whether it has metastasized rather than merely confirming its presence. Data originated from the Sichuan Province Cancer Hospital. The test, training, and validation sets were composed of 53 patients with 173 images, 195 patients with 1247 images, and 20 patients with 87 images, respectively. We created a deep learning network architecture consisting of encoders and decoders. To enhance global information capture, we substituted traditional convolutional decoders with global attention decoders and incorporated effective channel information fusion for multiscale information integration. The Dice coefficient (DSC) of the proposed model was 75.49%, which was 4.03% greater than that of the benchmark model, and the Hausdorff distance 95(HD95) was 24.75, which was 8.43 lower than that of the benchmark model. The paired t-test statistically confirmed the significance of the difference between our model and the benchmark model, with a p-value less than 0.05. The proposed method effectively identifies and segments rectal tumours of diverse shapes. Furthermore, it distinguishes between normal rectal images and those containing tumours. Therefore, after consultation with physicians, we believe that our method can effectively assist physicians in diagnosing rectal tumours via ultrasound. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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