Manjunatha Yashwanth, Sharma Vanshali, Iwahori Yuji, Bhuyan M K, Wang Aili, Ouchi Akira, Shimizu Yasuhiro
Dept. of Electronics & Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, 781039, India.
Dept. of Computer Science & Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, 781039, India.
Int J Comput Assist Radiol Surg. 2023 Apr;18(4):723-732. doi: 10.1007/s11548-022-02822-w. Epub 2023 Jan 11.
Lymph node (LN) detection is a crucial step that complements the diagnosis and treatments involved during cancer investigations. However, the low-contrast structures in the CT scan images and the nodes' varied shapes, sizes, and poses, along with their sparsely distributed locations, make the detection step challenging and lead to many false positives. The manual examination of the CT scan slices could be time-consuming, and false positives could divert the clinician's focus. To overcome these issues, our work aims at providing an automated framework for LNs detection in order to obtain more accurate detection results with low false positives.
The proposed work consists of two stages: candidate generation and false positive reduction. The first stage generates volumes of interest (VOI) of probable LN candidates using a modified U-Net with ResNet architecture to obtain high sensitivity but with the cost of increased false positives. The second-stage processes the obtained candidate LNs for false positive reduction using 3D convolutional neural network (CNN) classifier. We further present an analysis of various deep learning models while decomposing 3D VOI into different representations.
The method is evaluated on two publicly available datasets containing CT scans of mediastinal and abdominal LNs. Our proposed approach yields sensitivities of 87% at 2.75 false positives per volume (FP/vol.) and 79% at 1.74 FP/vol. with the mediastinal and abdominal datasets, respectively. Our method presented a competitive performance in terms of sensitivity compared to the state-of-the-art methods and encountered very few false positives.
We developed an automated framework for LNs detection using a modified U-Net with residual learning and 3D CNNs. The results indicate that our method could achieve high sensitivity with relatively low false positives, which helps avoid ineffective treatments.
淋巴结（LN）检测是癌症检查中辅助诊断和治疗的关键步骤。然而，CT扫描图像中的低对比度结构、淋巴结的形状、大小和姿态各异，且分布稀疏，使得检测步骤具有挑战性，并导致许多假阳性。人工检查CT扫描切片可能很耗时，而且假阳性会分散临床医生的注意力。为了克服这些问题，我们的工作旨在提供一个用于淋巴结检测的自动化框架，以获得更准确的检测结果且假阳性率低。
所提出的工作包括两个阶段：候选生成和假阳性减少。第一阶段使用具有ResNet架构的改进型U-Net生成可能的淋巴结候选者的感兴趣体积（VOI），以获得高灵敏度，但代价是假阳性增加。第二阶段使用3D卷积神经网络（CNN）分类器对获得的候选淋巴结进行假阳性减少处理。我们在将3D VOI分解为不同表示形式时，进一步对各种深度学习模型进行了分析。
该方法在两个包含纵隔和腹部淋巴结CT扫描的公开可用数据集上进行了评估。我们提出的方法在纵隔和腹部数据集上分别产生了每体积2.75个假阳性（FP/vol.）时灵敏度为87%和每体积1.74个假阳性时灵敏度为79%的结果。与现有方法相比，我们的方法在灵敏度方面表现出有竞争力的性能，并且假阳性很少。
我们开发了一个使用具有残差学习的改进型U-Net和3D CNNs的淋巴结检测自动化框架。结果表明，我们的方法可以在相对较低的假阳性情况下实现高灵敏度，这有助于避免无效治疗。
