Wenhao Li , Bowen Zheng , Quanyou Shen , Xiaoran Shi , Kun Luo , Yuqian Yao , Xinyan Li , Shidong Lv , Jie Tao , Qiang Wei
{"title":"Adaptive window adjustment with boundary DoU loss for cascade segmentation of anatomy and lesions in prostate cancer using bpMRI","authors":"Wenhao Li , Bowen Zheng , Quanyou Shen , Xiaoran Shi , Kun Luo , Yuqian Yao , Xinyan Li , Shidong Lv , Jie Tao , Qiang Wei","doi":"10.1016/j.neunet.2024.106831","DOIUrl":null,"url":null,"abstract":"<div><div>Accurate segmentation of prostate anatomy and lesions using biparametric magnetic resonance imaging (bpMRI) is crucial for the diagnosis and treatment of prostate cancer with the aid of artificial intelligence. In prostate bpMRI, different tissues and pathologies are best visualized within specific and narrow ranges for each sequence, which have varying requirements for image window settings. Currently, adjustments to window settings rely on experience, lacking an efficient method for universal automated adjustment. Hence, we propose an Adaptive Window Adjustment (AWA) module capable of adjusting window settings to accommodate different image modalities, sample data, and downstream tasks. Moreover, given the pivotal role that loss functions play in optimizing model performance, we investigate the performance of different loss functions in segmenting prostate anatomy and lesions. Our study validates the superiority of the Boundary Difference over Union (DoU) Loss in these tasks and extends its applicability to 3D medical imaging. Finally, we propose a cascaded segmentation approach tailored for prostate anatomy and lesions. This approach leverages anatomical structure information to enhance lesion segmentation accuracy. Experimental results on the Prostate158, ProstateX, and PI-CAI datasets confirm the effectiveness of the proposed methods. Our code of methods is available at <span><span>https://github.com/WenHao-L/AWA_BoundaryDoULoss</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"181 ","pages":"Article 106831"},"PeriodicalIF":6.0000,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neural Networks","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S089360802400755X","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Accurate segmentation of prostate anatomy and lesions using biparametric magnetic resonance imaging (bpMRI) is crucial for the diagnosis and treatment of prostate cancer with the aid of artificial intelligence. In prostate bpMRI, different tissues and pathologies are best visualized within specific and narrow ranges for each sequence, which have varying requirements for image window settings. Currently, adjustments to window settings rely on experience, lacking an efficient method for universal automated adjustment. Hence, we propose an Adaptive Window Adjustment (AWA) module capable of adjusting window settings to accommodate different image modalities, sample data, and downstream tasks. Moreover, given the pivotal role that loss functions play in optimizing model performance, we investigate the performance of different loss functions in segmenting prostate anatomy and lesions. Our study validates the superiority of the Boundary Difference over Union (DoU) Loss in these tasks and extends its applicability to 3D medical imaging. Finally, we propose a cascaded segmentation approach tailored for prostate anatomy and lesions. This approach leverages anatomical structure information to enhance lesion segmentation accuracy. Experimental results on the Prostate158, ProstateX, and PI-CAI datasets confirm the effectiveness of the proposed methods. Our code of methods is available at https://github.com/WenHao-L/AWA_BoundaryDoULoss.
期刊介绍:
Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.