Automatic Detection and Segmentation of Lymph Nodes From CT Data
Automatic Detection and Segmentation of Lymph Nodes From CT Data
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DOI：
10.1109/tmi.2011.2168234
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发表时间：
2012-02-01
影响因子：
10.6
通讯作者：
Comaniciu, Dorin
中科院分区：
文献类型：
--
作者：
Barbu, Adrian;Suehling, Michael;Comaniciu, Dorin
在临床实践中常规评估淋巴结，并在整个放疗或化疗过程中跟踪其大小，以监测癌症治疗的有效性。本文提出了一种鲁棒的基于学习的方法，从CT数据中自动检测和分割的固体淋巴结，具有以下贡献。首先，它提出了一种基于学习的方法来进行实体淋巴结检测，该方法依赖于边缘空间学习来实现很大的加速，几乎没有准确性损失。其次，它提出了一种计算效率高的分割方法的固体淋巴结（LN）。第三，它引入了两组新的特征，这些特征对LN检测是有效的，一组是自对准到高梯度的，另一组是从分割结果中获得的。该方法进行了评估腋窝LN检测131卷含有371 LN，产生83.0%的检出率与1.0假阳性每卷。进一步评价了在含有569个LN的54个体积上的盆腔和腹部LN检测，得到80.0%的检测率，每个体积有3.2个假阳性。每个体积的运行时间为5-20 s（腋窝区域）和15-40 s（骨盆区域）。该方法的另一个好处是能够检测和分割聚集的淋巴结。
Lymph nodes are assessed routinely in clinical practice and their size is followed throughout radiation or chemotherapy to monitor the effectiveness of cancer treatment. This paper presents a robust learning-based method for automatic detection and segmentation of solid lymph nodes from CT data, with the following contributions. First, it presents a learning based approach to solid lymph node detection that relies on marginal space learning to achieve great speedup with virtually no loss in accuracy. Second, it presents a computationally efficient segmentation method for solid lymph nodes (LN). Third, it introduces two new sets of features that are effective for LN detection, one that self-aligns to high gradients and another set obtained from the segmentation result. The method is evaluated for axillary LN detection on 131 volumes containing 371 LN, yielding a 83.0% detection rate with 1.0 false positive per volume. It is further evaluated for pelvic and abdominal LN detection on 54 volumes containing 569 LN, yielding a 80.0% detection rate with 3.2 false positives per volume. The running time is 5-20 s per volume for axillary areas and 15-40 s for pelvic. An added benefit of the method is the capability to detect and segment conglomerated lymph nodes.