利用基於深度學習之DRA-UNet模型做乳房超音波影像腫瘤區塊切割

Tumor Area semantic segmentation of breast ultra-sound images using DRA-UNet based on deep learning

何岳庭(Yueh-Ting Ho)

指導教授 ： 鐘太郎

國立清華大學/電機資訊學院/電機工程學系所/碩士(2021年)

摘要

乳癌是相當常見的癌症，在臺灣女性死因統計位居前三名，大大地對女性健康造成威脅。根據統計，自民國九十八年至一百零八年，隨著發病率的增加，超音波影像也被廣泛應用於分割乳房腫塊，因為其具有安全、無痛、非侵入性、無輻射等優點，且相較諸如CT、MRI影像，超音波影像費用便宜、可攜性較高，同時也更為普遍。然而；超音波影像需要具備相關經驗之放射學家的主觀判斷，人為標註費工且耗時的情況導致資料總量稀少，也為使用深度學習來分析超音波影像帶來更多的挑戰。
近年，深度學習在各式各樣的醫學影像切割任務中展現巨大的潛能，隨著超音波影像相關的醫療儀器往微型化、高效率、便於日常使用的可攜式裝備發展，深度學習的模型也應竭盡所能地輕量化，在耗時、準確性、穩定性中取得平衡。其中，以U-Net為骨幹的模型尤其突出，故本論文嘗試將六種用於其他醫學影像之神經網路，結合乳房超音波原始影像及其Ground truth，透過端對端的訓練得出各自對應的權重，對逐一像素分類，並從中取其優點予以結合，開發出一種新穎的神經網路架構: DRA-UNet (Dense-Res-Attention UNet)，可以幫助專業人士在超音波影像中圈選出腫瘤區域。
為了客觀分析腫瘤切割的結果，根據JSI、DSC、ACC、TPR、TNR、Precision六種錯誤指標去評估模型切割的好壞，本論文提出之DRA-UNet平均具有78.10%的JSI、85.79%的DSC、97.81%的ACC以及89.72%的Precision，四項指標為所有方法之首，88.31%的TPR落後於89.47%的Attention UNet、98.73%的TNR落後於98.79%的MultiResUNet，其餘兩項指標為第二名，因此可證實該方法可用於生醫影像應用中，且能基於現存的方法做出改善，在乳癌早期階段提供對腫瘤、病灶適當的檢測。
綜上所述，本論文提出之方法具備以下優點: 其一，訓練過後的模型不需人工調適參數，使用全自動切割系統幫助醫師及放射師判斷，可以節省寶貴的醫護人力與時間；其二，使用較少的參數量、稀少的訓練資料也能有優良的像素級別切割能力；其三，在病灶極小、音響陰影嚴重等難易度較高的超音波影像，依然能保持穩定的水準。

關鍵字

超音波乳房影像 ； 腫瘤切割 ； 語意切割 ； 深度學習 ； Dense-Res-Attention UNet(DRA-UNet) ； UNet ； Fully convolution network(FCN)

並列摘要

According to statistics in Taiwan, breast cancer is the third-commonest reason which causes death. This fact really pose a significant threat to women’s health. From 2009 to 2019, with the increment of death rate, medical ultrasound imaging has been widely employed to segment breast lumps because its safety, painless characteristics, noninvasive diagnosis and non-ionized radiation. Furthermore, compared with other clinical medical imaging such as CT and MRI, ultrasound is relatively cheaper, portable and general-used. Nevertheless, it requires the subjective judgement from radiologists with relevant experience and the annotation is laborious and time-consuming, resulting in scarcity of data and bringing more challenges for implementing deep learning technologies on analyzing ultrasound images.
In recent years, deep learning in computer vision has demonstrated the potential in a vast repertoire of biomedical image segmentation tasks. With the development of medical equipment, professionals prefer to miniaturize the ultrasound devices to leverage the efficiency and portability. Therefore, the deep learning models should also be as lightweight as possible to strike a balance among time consumption, accuracy and stability. In this thesis, I used six existing neural networks for biomedical images and took breast ultrasound raw images and their ground truths to derive the corresponding weights through end-to-end training and categorize the lesions pixel by pixel in the end. Thanks to other brilliant structures that the great researchers proposed before, I combined them and developed a novel architecture, DRA-UNet (Dense-Res-Attention UNet), as a solution of assisting the professionals to delineate the tumor area.
For the sake of objectively analyzing the results of tumor segmentation, the six error metrics of JSI, DSC, ACC, TPR, TNR, and Precision were used to evaluate the goodness of the model. The DRA-UNet has the highest JSI of 78.10%, DSC of 85.79%, ACC of 97.81% and Precision of 89.72%, while TPR of 89.47% and TNR of 98.79% are the second best. Thus, the proposed method really can be improved based on existing methods and provide appropriate detection of tumors and lesions at early stages of breast cancer.
In summary, the proposed method has the following advantages. First, the model does not require any manual adjustment, which saves valuable healthcare manpower and time; second, it has excellent pixel-level segmenting ability even with a small number of parameters and sparse training data; third, it can maintain a stable level in ultrasound images with high difficulty, such as very tiny lesions and severe acoustic shadowing.

並列關鍵字

Breast ultrasound images ； Tumor segmentation ； Dense-Res-Attention UNet(DRA-UNet) ； Semantic segmentation ； Deep learning ； UNet ； Fully convolution network(FCN)

參考文獻

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