在科技充斥且發達的現代社會，人類每天的生活也仰賴著科技進步所帶來的幫助，像是餐廳裡的自助點餐機、工廠裡的機械手臂，還有線上或者是醫院裡面的自動診斷系統，人們藉由科技為生活帶來了許多便利，也幫助我們更能有效率完成許多事務。
而在醫院中的自動診斷系統，要能發揮到真正幫助到人類的效果，就必須要有足 夠準確的判斷結果。本論文針對手骨 X 光影像腕骨分割問題進行探討，透過結合 ResNeSt 和 U-net 神經網路架構的優點，並且也在損失函數上進行利用 Dice Loss 和 Boundary Loss 的相加，使得分割結果能在邊界上有更好的表現，提出可快速、準確 的自動化分割出腕骨部分之系統，並期望若是將來可與手部 X 光影像的判讀做結合， 那麼將可為放射科醫護人員帶來便利，能更有效率、更準確地做出判斷，並且降低因 人為因素、或者影像不夠清晰所造成的誤判。

In a modern society with advanced technology, humans’ daily lives rely on the help brought by technology, such as self-service ordering machines in restaurants, robotic arms in factories, and automatic diagnosis systems online or in hospitals. People use technology to bring a lot of convenience to life, and also help us to complete many tasks more efficiently.
However, if the automatic diagnosis systems in the hospital can really help human beings, it must have sufficiently accurate judgment results. This thesis focuses on hand X-ray images. By combining the advantages of ResNeSt and U-net neural network architecture, and also using the addition of Dice Loss and Boundary Loss in the loss function, the segmentation results can be better on the boundary performance. This thesis proposes a system that can automatically segment the carpal bones quickly and accurately. If the hand X-ray images can be combined with the interpretation system in the future, it will enable radiologists to make more efficient and accurate judgments. Using machines to help humans make more objective judgments can reduce misjudgments caused by human factors or images that are not clear enough.

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