由影像處理方法量化新生兒呼吸道窘迫症候群之胸腔X光片嚴重度

Abstract

目前新生兒呼吸窘迫症候群 (respiratory distress syndrome, 縮寫為 RDS)為早 產兒死亡率最高的幾個病因之一，其症狀為在出生後六小時內逐漸現呼吸困難、皮膚青紫化，呼氣性呻吟吸動作不協調等在 48小時內死亡率較高。 然而因症狀並非出生後即顯現，是漸進加重此需要事先經由輔助檢查評估是否需要進行臨床治療。目前使用的輔助檢測法包含羊水查、泡沫試驗羊水磷脂甘油測定、血液檢查及 CXR(Chest X-ray, 即胸腔 X光片 )檢查等，其 檢查等，其 中除了 CXR外的各試驗皆有具體數值去定義 RDS的嚴重程度，如泡沫試驗即 計算搖盪後的泡沫產生數、羊水及血液檢查可出具體化合物濃度，唯 CXR的嚴重程度為定性而非量分析，目前臺大醫院所使用四級法別依 a. 肺部網狀小顆粒浸潤與否 b. 肺部血管充與否 c. 橫膈與 心肺消失與否 來進 行分級。因此本研究嘗試針對臨床上出現呼吸窘迫象的新生兒之胸腔 X光片 進行數值化，以期可建立比較客觀的分級方法。 醫學影像辨識為一件頗複雜的工作， 尤其涉及病變而出現不規則性與一般影像處理相比有 更多的限制。而RDS現象的新生兒，在其胸腔 X光 片上會出現白化、邊緣模糊的象，讓諸多行處理胸腔肺部擷取方法無使用，且因為新生兒的體型關係成象時射線強度較年人低骨骼及肌肉等軟組織的對比較小，必須要另想方法才能取出肺部區域。 我們使用針對肋骨而設計的線偵測遮罩標定出區域後 ，再由邊緣跟隨 的方式走出大略胸腔範圍。此外以脊椎與肋骨相連特性，利用閥值尋找脊椎的位置。綜合以上方法最後可大略取出肺部區域，對所選塊計算其平均及標準差。最後 建立數值模型，排序其 數值， 與醫師分級之一致性可達 86%，說明了此檢測法量化之可能 。

Respiratory distress syndrome is one of the main cause of death for premature baby. Its symptoms include difficulty breathing, purple skin, expiratory moan and uncoordinated breathing. It’s mortality rate is highest in the first 48 hours. However, the symptoms are not shown immediately after birth. Some auxiliary examination is required for early detection of RDS. The auxiliary examination includes amniotic fluid examination, bubble test, PG examination, blood examination and chest x-ray(CXR) examination etc. All auxiliary examinations exclude CXR examination have specific numbers for classification, as the number of bubbles in bubble test, the molecule density in PG, amniotic and blood examination. CXR is a qualify but not quantify examination. The four grades classification standard used by NTUH are as follow: 1. If frosted glass pattern appear 2. If vessels are congested. 3. if the boundary between heart and lung, diaphragm and lung disappears. This research tries to numerically describe the RDS level of CXR, hoping to find a quantitative method for CXR classification. Medical image processing is not an easy work, especially for the case involving irregularity disease area. For RDS new born, there will be white out and boundary blurring effect on CXR. It makes most of the methods used for segmentation for adult CXR invalid. New approach must be developed. We use filter that are sensitive to ribs and use edge following technique to find the approximate region of thoracic cage. And by use of the property that spine nodes will be connected to ribs, to segment out the spine region by threshold. By mixing all approaches above, the lung area can be roughly segment out. After calculating the average and variation of selected regions, we see a correspondence (87% correctness) between the numerically grading and the qualitative grading which decided by doctors. It is concluded that it is possible to quantify RDS CXR by image processing.