中文标题#
基於 Hessian 的輕量級神經網絡在最小訓練數據集上的腦血管分割
英文标题#
Hessian-based lightweight neural network for brain vessel segmentation on a minimal training dataset
中文摘要#
在腦磁共振血管造影（MRA）中準確分割血管對於成功的手術程序（如動脈瘤修復或搭橋手術）至關重要。目前，註釋主要通過手動分割或經典方法（如 Frangi 濾波器）進行，這些方法通常準確性不足。神經網絡已成為醫學圖像分割的強大工具，但其發展依賴於標註良好的訓練數據集。然而，缺乏公開可用的具有詳細腦血管註釋的 MRA 數據集。為解決這一差距，我們提出了一種基於 Hessian 矩陣的新型半監督學習輕量級神經網絡，用於複雜結構（如管狀結構）的 3D 分割，我們將其命名為 HessNet。該解決方案是一個基於 Hessian 的神經網絡，僅有 6000 個參數。HessNet 可以在 CPU 上運行，並顯著降低訓練神經網絡的資源需求。在最小訓練數據集上的血管分割準確性達到了最先進的結果。它幫助我們創建了一個大型的半自動標註的腦 MRA 圖像腦血管數據集，基於 IXI 數據集（標註了 200 張圖像）。在應用 HessNet 後，由三位專家在三位神經血管外科醫生的監督下進行註釋。它提供了高精度的血管分割，並使專家只需關注最複雜的重點案例。該數據集可在https://git.scinalytics.com/terilat/VesselDatasetPartly 獲取。
英文摘要#
Accurate segmentation of blood vessels in brain magnetic resonance angiography (MRA) is essential for successful surgical procedures, such as aneurysm repair or bypass surgery. Currently, annotation is primarily performed through manual segmentation or classical methods, such as the Frangi filter, which often lack sufficient accuracy. Neural networks have emerged as powerful tools for medical image segmentation, but their development depends on well-annotated training datasets. However, there is a notable lack of publicly available MRA datasets with detailed brain vessel annotations. To address this gap, we propose a novel semi-supervised learning lightweight neural network with Hessian matrices on board for 3D segmentation of complex structures such as tubular structures, which we named HessNet. The solution is a Hessian-based neural network with only 6000 parameters. HessNet can run on the CPU and significantly reduces the resource requirements for training neural networks. The accuracy of vessel segmentation on a minimal training dataset reaches state-of-the-art results. It helps us create a large, semi-manually annotated brain vessel dataset of brain MRA images based on the IXI dataset (annotated 200 images). Annotation was performed by three experts under the supervision of three neurovascular surgeons after applying HessNet. It provides high accuracy of vessel segmentation and allows experts to focus only on the most complex important cases. The dataset is available at https://git.scinalytics.com/terilat/VesselDatasetPartly.
文章页面#
基於 Hessian 的輕量級神經網絡在最小訓練數據集上的腦血管分割
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