2D View Aggregation for Lymph Node Detection Using a Shallow Hierarchy of Linear Classifiers

Enlarged lymph nodes (LNs) can provide important information for cancer diagnosis, staging, and measuring treatment reactions, making automated detection a highly sought goal. In this paper, we propose a new algorithm representation of decomposing the LN detection problem into a set of 2D object detection subtasks on sampled CT slices, largely alleviating the curse of dimensionality issue. Our 2D detection can be effectively formulated as linear classification on a single image feature type of Histogram of Oriented Gradients (HOG), covering a moderate field-of-view of 45 by 45 voxels. We exploit both simple pooling and sparse linear fusion schemes to aggregate these 2D detection scores for the final 3D LN detection. In this manner, detection is more tractable and does not need to perform perfectly at instance level (as weak hypotheses) since our aggregation process will robustly harness collective information for LN detection. Two datasets (90 patients with 389 mediastinal LNs and 86 patients with 595 abdominal LNs) are used for validation. Cross-validation demonstrates 78.0% sensitivity at 6 false positives/volume (FP/vol.) (86.1% at 10 FP/vol.) and 73.1% sensitivity at 6 FP/vol. (87.2% at 10 FP/vol.), for the mediastinal and abdominal datasets respectively. Our results compare favorably to previous state-of-the-art methods.Comment: This article will be presented at MICCAI (Medical Image Computing and Computer-Assisted Intervention) 201

Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical priors guiding

As lung cancer evolves, the presence of potentially malignant lymph nodes must be assessed to properly estimate disease progression and select the best treatment strategy. A method for accurate and automatic segmentation is hence decisive for quantitatively describing lymph nodes. In this study, the use of 3D convolutional neural networks, either through slab-wise schemes or the leveraging of downsampled entire volumes, is investigated. As lymph nodes have similar attenuation values to nearby anatomical structures, we use the knowledge of other organs as prior information to guide the segmentation. To assess the performances, a 5-fold cross-validation strategy was followed over a dataset of 120 contrast-enhanced CT volumes. For the 1178 lymph nodes with a short-axis diameter ≥10 mm, our best-performing approach reached a patient-wise recall of 92%, a false positive per patient ratio of 5 and a segmentation overlap of 80.5%. Fusing a slab-wise and a full volume approach within an ensemble scheme generated the best performances. The anatomical priors guiding strategy is promising, yet a larger set than four organs appears needed to generate an optimal benefit. A larger dataset is also mandatory given the wide range of expressions a lymph node can exhibit (i.e. shape, location and attenuation).publishedVersio

Preoperative nodal staging of non-small cell lung cancer using 99mTc-sestamibi spect/ct imaging

OBJECTIVES: The proper nodal staging of non-small cell lung cancer is important for choosing the best treatment modality. Although computed tomography remains the first-line imaging test for the primary staging of lung cancer, its limitations for mediastinum nodal staging are well known. The aim of this study is to evaluate the accuracy of hybrid single-photon emission computed tomography and computed tomography using 99mTc-sestamibi in the nodal staging of patients with non-small cell lung cancer and to identify potential candidates for surgical treatment. METHODS: Prospective data were collected for 41 patients from December 2006 to February 2009. The patients underwent chest computed tomography and single-photon emission computed tomography/computed tomography examinations with 99mTc-sestamibi within a 30-day time period before surgery. Single-photon emission computed tomography/computed tomography was considered positive when there was focal uptake of sestamibi in the mediastinum, and computed tomography scan when there was lymph nodes larger than 10 mm in short axis. The results of single-photon emission computed tomography and computed tomography were correlated with pathology findings after surgery. RESULTS: Single-photon emission computed tomography/computed tomography correctly identified six out of 19 cases involving hilar lymph nodes and one out of seven cases involving nodal metastases in the mediastinum. The sensitivity, specificity, positive predictive value, and negative predictive value for 99mTc-sestamibi single-photon emission computed tomography/computed tomography in the hilum assessment were 31.6%, 95.5%, 85.7%, and 61.8%, respectively. The same values for the mediastinum were 14.3%, 97.1%, 50%, and 84.6%, respectively. For the hilar and mediastinal lymph nodes, chest tomography showed sensitivity values of 47.4% and 57.1%, specificity values of 95.5% and 91.2%, positive predictive values of 90% and 57.1% and negative predictive values of 67.7% and 91.2%, respectively. CONCLUSION: Single-photon emission computed tomography/computed tomography with 99mTc-sestamibi showed very low sensitivity and accuracy for the nodal staging of patients with non-small cell lung cancer, despite its high level of specificity. In addition, the performance of single-photon emission computed tomography/computed tomography added no relevant information compared to computed tomography that would justify its use in the routine preoperative staging of non-small cell lung carcinoma

Anatomy-Aware Lymph Node Detection in Chest CT using Implicit Station Stratification

Finding abnormal lymph nodes in radiological images is highly important for various medical tasks such as cancer metastasis staging and radiotherapy planning. Lymph nodes (LNs) are small glands scattered throughout the body. They are grouped or defined to various LN stations according to their anatomical locations. The CT imaging appearance and context of LNs in different stations vary significantly, posing challenges for automated detection, especially for pathological LNs. Motivated by this observation, we propose a novel end-to-end framework to improve LN detection performance by leveraging their station information. We design a multi-head detector and make each head focus on differentiating the LN and non-LN structures of certain stations. Pseudo station labels are generated by an LN station classifier as a form of multi-task learning during training, so we do not need another explicit LN station prediction model during inference. Our algorithm is evaluated on 82 patients with lung cancer and 91 patients with esophageal cancer. The proposed implicit station stratification method improves the detection sensitivity of thoracic lymph nodes from 65.1% to 71.4% and from 80.3% to 85.5% at 2 false positives per patient on the two datasets, respectively, which significantly outperforms various existing state-of-the-art baseline techniques such as nnUNet, nnDetection and LENS