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Author: 張海川
Hai-Chuan Zhang
Thesis Title: 基於雙輸出網路的胎兒頭部超音波影像分割測量方法
Ultrasound Fetal Head Segmentation and Measurement Method Using Dual Output Networks
Advisor: 洪西進
Shi-Jinn Horng
Committee: 謝仁偉
Jen-Wei Hsieh
Cheng-Chi Lee
Chu-Hsing Lin
Yi-Leh Wu
Chu-Sing Yang
Degree: 碩士
Department: 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
Thesis Publication Year: 2022
Graduation Academic Year: 110
Language: 中文
Pages: 56
Keywords (in Chinese): 深度學習神經網絡圖像分割
Keywords (in other languages): Deep learning, Neural networks, Image segmentation
Reference times: Clicks: 210Downloads: 7
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本研究提出了一個基於Deeplab V3 Plus的雙輸出網路用於實現胎兒頭部超音波影像的頭圍、顱骨端到端分割方法,並提出了利用傳統形態學方法生成並製作胎兒顱骨分割標註資料集的方法解決上述問題。本研究對胎兒頭圍和顱骨的型態結構關聯性提出相互促進的LOLI Loss,幫助網路實現更高的分割準確度。本研究亦對當前流行的多種骨幹網路與Attention機制進行實驗,以探討這些骨幹網路和Attention機制對於卷積神經網絡的胎兒頭圍-顱骨分割之價值。

As high-performance Graphic Processing Unit was widely used on medical devices with rapid replacement of ultrasound machine, automatic measurements such as object detection, image classification and semantic segmentation based on artificial intelligence technology play a significant role on novel medical equipment.

For fetal head measurement, several morphology-based and traditional statistics methods had been used in the past; unfortunately, imprecise prediction result and long executing time usually occurred with these models. Although some deep-learning-based algorithms have been proposed to annotate head circumference for getting occipitofrontal diameter, morphology-based processing should also be utilized to estimate skull thickness for calculating biparietal diameter which is costing large amounts of time while there is no skull annotation dataset provided until the publication of this study.

In this study, we have proposed an end-to-end deep-learning based method named Dual-output Networks designed for annotating head circumference and skull simultaneously. Also, a novel way to generate and process skull annotation from fetal head ultrasound image has been raise. Moreover, a loss function called LOLI Loss based on morphological association of head circumference and skull has been utilized to improve accuracy of prediction. Several commonly used backbone models and attention module have been tried to verify if there is any benefit to improve its precision.

第一章 緒論 9 第二章 相關研究 11 第三章 研究方法 13 3.1 網路架構 13 3.1.1 BackBone骨幹網路 16 3.1.2 單輸出網路 18 3.1.3 雙輸出網路 18 3.2 資料集的生成與製作 20 3.3 Attention注意力機制設計 27 3.4 Loss Function損失函數設計 34 3.4.1 基於像素差異的損失函數 38 3.4.2 基於距離差距的損失函數 38 3.4.3 基於雙輸出相互促進的損失函數(LOLI Loss) 38 第四章 實驗結果 38 4.1 系統架構及硬體規格 38 4.2 實驗資料集及其預處理設計 40 4.3 評量指標 42 4.4 消融實驗 43 4.4.1 不同骨幹網路之效果對比 44 4.4.2 不同Attention機制之效果對比 45 4.4.3 不同LOLI Loss Coefficient之效果對比 47 4.4.4 混合實驗 48 4.5 C++項目的整合、部署與運用實驗 49 第五章 結論 51 5.1 研究成果 51 5.2 未來展望 52 參考資料 54

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