北京生物醫(yī)學(xué)工程

基于全卷積神經(jīng)網(wǎng)絡(luò)的直腸癌腫瘤磁共振影像自動(dòng)分割方法

Automatic segmentation method based on full convolution neural network for rectal cancer tumors in magnetic resonance image

作者：冉昭簡俊明王蒙蒙趙星羽高欣

單位：中國科學(xué)技術(shù)大學(xué)（合肥 230026）中國科學(xué)院蘇州生物醫(yī)學(xué)工程技術(shù)研究所（蘇州215163）

關(guān)鍵詞：直腸腫瘤分割；神經(jīng)網(wǎng)絡(luò)；多邊輸出；磁共振影像

分類號：R318.04

出版年·卷·期（頁碼）：2019·38·5（465-471）

摘要：

目的直腸腫瘤（rectum cancer, RC）的圖像精確分割是直腸癌診斷和治療的基礎(chǔ)和關(guān)鍵。目前，直腸腫瘤的分割通常是由放射科醫(yī)生逐切片進(jìn)行，這種方式主觀性強(qiáng)，工作量大。為此，本文提出了一種直腸腫瘤磁共振影像全自動(dòng)分割網(wǎng)絡(luò)，在有效減少放射科醫(yī)生負(fù)擔(dān)的同時(shí)提高了腫瘤分割結(jié)果的可重復(fù)性。方法首先采用一個(gè)預(yù)訓(xùn)練的ResNet50提取特征，并在網(wǎng)絡(luò)隱藏層添加三個(gè)邊輸出模塊，實(shí)現(xiàn)圖像數(shù)據(jù)的多尺度特征提取，最后融合三個(gè)邊輸出模塊獲得最終的分割結(jié)果。將所提網(wǎng)絡(luò)架構(gòu)的分割結(jié)果與基于U-net網(wǎng)絡(luò)架構(gòu)的分割結(jié)果進(jìn)行比較，并分析不同損失函數(shù)和感興趣區(qū)域（region of interest, ROI）尺寸對所提網(wǎng)絡(luò)分割性能的影響。結(jié)果本研究使用中山大學(xué)附屬第六醫(yī)院512例患者的影像數(shù)據(jù)對模型進(jìn)行訓(xùn)練及測試，其中隨機(jī)選取的461名患者的T2加權(quán)磁共振影像用于網(wǎng)絡(luò)訓(xùn)練，剩下51名患者的T2加權(quán)磁共振影像用于網(wǎng)絡(luò)測試。結(jié)果表明，所提網(wǎng)絡(luò)分割結(jié)果的平均Dice相似性系數(shù)（Dice similarity coefficient, DSC）、平均敏感度（sensitivity）、平均特異度（specificity）及平均豪斯多夫距離（Hausdorff distance, HD）分別達(dá)到了83.61%、89.10%、96.36%和8.49，均優(yōu)于基于U-net的分割方法。對于包含了腫瘤組織的ROI，尺寸越小，分割效果越好。對于給定尺寸的ROI，幾種損失函數(shù)并無太大差異。結(jié)論該算法能夠準(zhǔn)確地勾畫腫瘤邊界，將有助于提升醫(yī)生工作效率。

Objective Accurate segmentation of rectal tumors form images is a basic and crucial task for diagnosis and treatment of rectal cancer. Currently, the segmentation of rectal tumors is usually performed by radiologists on a slice-by-slice basis, which is highly subjective and requires a large amount of work. Therefore, this paper proposes an automatic segmentation network for magnetic resonance image of rectal tumors, which can not only effectively reduce the burden of radiologists, but also improved the repeatability of tumor segmentation results. Methods A pre-trained Resnet50 model was introduced for feature extraction, and three side-output modules were added to the hidden layer of Resnet50 to guide multi-scale feature learning. The final boundaries of tumor were determined by the fusion of the predictions from side-output modules. The proposed model was compared with a U-net based model, and the impacts of different region of interest (ROI) sizes and loss functions were also evaluated. Results We trained and evaluated the models on the data of 512 patients from Sixth Affiliated Hospital of Sun Yat-sen University (Guangzhou, China), in which the T2-weighted magnetic resonance images (T2W-MRIs) of 461 patients were randomly selected for model training, while the T2W-MRIs of the remain 51 patients were used for performance evaluation. The proposed model was superior to the U-net based model and achieved an average Dice similarity coefficient of 83.61%, an average sensitivity of 89.10%, an average specificity of 96.36%, and an average Hausdorff distance of 8.49. In addition, when the ROI contained rectal tumor tissue, the smaller the ROI size was, the higher the segmentation accuracy would be. For a certain ROI size, there were no significant differences in segmentation results among these loss functions. Conclusion The proposed network can accurately delineate the tumor boundaries and could help improve physician productivity.

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