北京生物醫(yī)學(xué)工程

基于稠殘U-net神經(jīng)網(wǎng)絡(luò)在定位CT圖像上自動(dòng)分割甲狀腺的研究

Study on automatic thyroid segmentation based on thick residue U-net neural network in localized CT images

作者：袁美芳楊毅趙彪文曉博易三莉

單位：昆明理工大學(xué)信息工程與自動(dòng)化學(xué)院（云南昆明  650500）云南省腫瘤醫(yī)院放射治療科（云南昆明  650118） 通信作者：易三莉。E-mail：[email protected]

關(guān)鍵詞：卷積神經(jīng)網(wǎng)絡(luò)；殘差塊；稠密連接；甲狀腺；CT圖像

分類(lèi)號(hào)：R318.04

出版年·卷·期（頁(yè)碼）：2022·41·1（42-48）

摘要：

目的基于深度學(xué)習(xí)方法提出一種稠殘U-net神經(jīng)網(wǎng)絡(luò)，探討其在放療定位CT上自動(dòng)預(yù)測(cè)甲狀腺輪廓的可行性，以減少放療中甲狀腺所受輻射劑量，降低甲減發(fā)生率。方法在U-net網(wǎng)絡(luò)中引入殘差機(jī)制和稠密連接機(jī)制建立一種稠殘U-net網(wǎng)絡(luò)。選取76名患者定位CT圖像的甲狀腺切片制作數(shù)據(jù)集，隨機(jī)劃分為訓(xùn)練集58例、驗(yàn)證集9例和測(cè)試集9例，對(duì)稠殘U-net進(jìn)行訓(xùn)練、驗(yàn)證和測(cè)試，得到稠殘U-net自動(dòng)預(yù)測(cè)甲狀腺的結(jié)果。通過(guò)戴斯相似性系數(shù)（Dice）、杰卡德相似系數(shù)（Jaccard)）和豪斯多夫距離（HD）等評(píng)價(jià)指標(biāo)來(lái)評(píng)估其分割性能。結(jié)果稠殘U-net預(yù)測(cè)甲狀腺的Dice值為0.86±0.09、Jaccard值為0.78±0.12、HD值為2.52±0.61，且預(yù)測(cè)的輪廓邊界與專(zhuān)家勾畫(huà)的標(biāo)準(zhǔn)邊界非常接近。結(jié)論本文提出的稠殘U-net能在定位CT圖像上較為精準(zhǔn)地預(yù)測(cè)甲狀腺輪廓，且證明在卷積神經(jīng)網(wǎng)絡(luò)中引入殘差機(jī)制和稠密連接機(jī)制能提高其分割性能。

Objective Based on the deep learning method, a dense-residual U-net neural network is proposed to explore the feasibility of automatically predicting the contour of the thyroid on the CT images of radiotherapy positioning, so as to reduce the radiation dose to the thyroid during radiotherapy and reduce the incidence of hypothyroidism .Methods Residual mechanism and dense connection mechanism were introduced into U-net to establish dense- residual U-net neural network. The thyroid tissue structures of 76 patients were selected from localized CT images to make a data set, which were randomly divided into a training set of 58 patients, a verification set of 9 patients and a test set of 9 patients.The dense-residual U-net neural network was trained, verified and tested, and the thyroid contour predicted by neural network were obtained. The segment performance of dense-residual U-net were evaluated by dice, jaccard and HD.Results The thyroid’ s dice was 0.86±0.09, the jaccard was 0.78±0.12, and the HD was 2.52±0.61 by dense-residual U-net, and the predicted contour boundary was very close to the standard drawn by experts.Conclusions The dense-residual U-net proposed in this study can accurately predict the thyroid contour on localized CT images, and it is proved that the residual mechanism and dense connection mechanism can improve performance of convolutional neural network in medical image segmentation.

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