青光眼是由於視覺神經受損而造成的一系列眼部疾病，甚至會導致永久失去視力。
為了篩檢和診斷出青光眼，杯盤比(cup-to-disc ratio)成為一個關鍵的依據之一。
因此，能自動精準分割眼底鏡影像的視神經盤(Optic Disc)和視神經杯(Optic Cup)是必要的，也成為一個重要的議題。
過去的方法會依賴人工選取特徵的方式進行分割，或者是分別將視神經杯盤分別處理。

隨著深度學習發展，越來越多人提出深度模型來解決眼底鏡分割的任務，但效果容易侷限在某些資料集上。
在我們的論文中，我們提供一個輕量化的深度學習的架構解決同時分割視神經杯盤，同時使用極座標轉換和直方圖等化做為前處理簡化學習的複雜度。
我們的模型主要是使用編碼-解碼模型(Encoder-Decoder)的構造，其中包含我們提出的特徵抽取模組和多輸出模組。
在編碼器方面(Encoder)，特徵抽模組使用MobileNetV2的線性瓶頸元件達到簡化模型並維持抽取特徵的效果，再經ASPP擷取出代表不同尺度的特徵。
而解碼器方面(Decoder)，結合低層特徵(low-level feature)和ASPP的特徵有助於穩定分割的輪廓
，並使用多輸出模組透過不同的輸出和多標籤的損失函數來調整模型最終結果。

最後，實驗顯示我們的方法可以改善不同的深度網絡模型在是神經杯盤的分割在不同的相機影像上，其中包含 REFUGE、MESSIDOR和RIMONE的資料集。
同時，將我們的方法應用於REFUGE並經由杯盤比的計算進行篩檢青光眼也獲不錯的效果。

Glaucoma is caused by damaged optic nerves, and can lead to permanent vision loss. The cup-to-disk ratio (CDR) is a key criterion for glaucoma diagnosis, therefore an accurate automatic segmentation of the optic disc (OD) and optic cup (OC) in retinal fundus images has become a major research topic.

However, with deeper deep learning models being used to complete a fundus segmentation, the segmentation results remain acceptable only on certain datasets. In this research, a lightweight deep-learning encoder–decoder architecture, which adopts polar coordinate transformation and histogram equalization to simplify the learning complexity, is proposed for the simultaneous segmentation of OD and OC. The proposed model employs an encoder–decoder architecture consisting of a feature extraction module and a multi-output module to both simplify the model and to adjust the result through different outputs and multilabel loss functions.

Experiment results demonstrate that the proposed approach outperforms other deep network models on OD and OC segmentation over the datasets REFUGE, MESSIDOR, and RIM-ONE, which contain images captured from cameras with various specifications. In addition, better results on glaucoma screening through CDR calculation were obtained on the REFUGE dataset with the proposed method.

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