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Leveraging network using controlled weight learning approach for thyroid cancer lymph node detection

Kavitha Muthu Subash, Yudistira Novanto, Ahn Byeong-Cheol, Kurita Takio

Biocybernetics and Biomedical Engineering

2021

Vol. 41, no. 4

1589--1600

Identification of cervical metastatic lymph nodes (LN) on I-131 post-ablation whole-body planar scans (WBS) for cancer staging is crucial for patients with papillary thyroid cancer (PTC). The existing deep network is plagued by instability in finding the under-represented LN classes on highly complex WBS, where end-to-end tuned models that can account for this uncertainty region for multi-class locations are needed. Hence, as a key contribution of this study, we designed a novel leveraging segmentation network with input guidance (LSIG) end-to-end training model without pre and post-processing features that can learn ideal parameter settings depending on the quantity of multiple-object instances. To improve the co-occurrence of classes and control the false positive regions, we proposed a re-weighting negative control (RNC) mechanism that combines two key components, namely the re-weighting (Rw) term and the negative control function (NcF). This unified approximation of weighted training would leverage the network to control and learn the desired weights of true positives towards the LN region. As an end-to-end network training, we utilize a Unet-like convolution neural network (CNN) model. The performance of the LSIG is compared with the CNN-based networks, based on the ground truth (GT) mask developed using post-ablation single-photon emission computed tomography (SPECT/CT). Furthermore, the effectiveness of the two components used in the LSIG framework is evaluated on WBS datasets. Our proposed LSIG with a fully guided (Fg) LS-FgCNN model yielded a superior performance with high AUC value of 94.9%, which is 14.6% higher than the previous network for PTC.

An adaptive multi-scale network with nonorthogonal multi-union input for reducing false positive of lymph nodes

Meng Xingqi, Peng Yanjun, Guo Yanfei

Biocybernetics and Biomedical Engineering

2021

Vol. 41, no. 1

265--277

Lymph node (LN) detection and analysis are of great significance in terms of cancer staging and measuring the effectiveness of treatment. But, it is still a challenging and laborious task due to the lack of adequately-labeled data and the similar pathological features with surrounding structures in computed tomography (CT). In this paper, we propose a new representation for lymph node detection after augmentation, which can effectively decom-pose candidate cubic CT images by generating nonorthogonal multi-union 2D slices. These new views with coupling relationship will be used as a novel input to train the convolutional neural networks (CNNs) to achieve the purpose of reducing false positives (FP). In order to further adapt to the mutative radiological characteristics of lymph nodes, we designed an adaptive multi-scale network. This model adaptively learns the features of different scales images and redistributes weights of convolution kernel to optimize the classification result. We validate the approach on two datasets 90 CT volumes with 388 mediastinal LNs and 86 patients with 595 abdominal LNs. Our proposed methods perform better in both two cases, sensitivities reached 78%, 86% at 3 false-positives per patient volume (FP/vol.), and 94%, 96% at 6 FP/vol. in mediastinum and abdomen respectively.