Semi-automatic lymph node segmentation and classification using cervical cancer MR imaging

Bnouni, Nesrine; Mechi, Olfa; Rekik, Islem; Rhim, Mohamed Salah; Amara, Najoua Essoukri Ben

doi:10.1109/atsip.2018.8364480

Cited by 7 publications

(4 citation statements)

References 21 publications

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“…In [5] a method is proposed for axillary LN segmentation in CT using a classification of solid vs. non-solid LN depending on their tissue homogeneousness, considering that enlarged lymph nodes with a solid interior have a higher probability of being malignant. Other techniques such as [6] perform LN detection and segmentation in magnetic resonance images in the cervical area. These methods present promising LN segmentation results, mainly based on machine learning techniques using CT information.…”

Section: B Related Workmentioning

confidence: 99%

Segmentation of Axillary and Supraclavicular Tumoral Lymph Nodes in PET/CT: A Hybrid CNN/Component-Tree Approach

Cabrera

Gogin

Morland

et al. 2021

2020 25th International Conference on Pattern Recognition (ICPR)

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The analysis of axillary and supraclavicular lymph nodes is a primary prognostic factor for the staging of breast cancer. However, due to the size of lymph nodes and the low resolution of PET data, their segmentation is challenging. We investigate the relevance of considering axillary and supraclavicular lymph node segmentation from PET/CT images by coupling Convolutional Neural Networks (CNNs) and Component-Trees (C-Trees). Building upon the U-Net architecture, we propose a framework that couples a multi-modal U-Net fed with PET and CT with a hierarchical model obtained from the PET that provides additional high-level region-based features as input channels. Our working hypotheses are twofold. First, we take advantage of both anatomical information from CT for detecting the nodes, and functional information from PET for detecting the pathological ones. Second, we consider region-based attributes extracted from C-Tree analysis of 3D PET/CT images to improve the CNN segmentation. We carried out experiments on a dataset of 240 pathological lymph nodes from 52 patients scans, and compared our outputs with human expert-defined ground-truth, leading to promising results.

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Section: B Related Workmentioning

confidence: 99%

Segmentation of Axillary and Supraclavicular Tumoral Lymph Nodes in PET/CT: A Hybrid CNN/Component-Tree Approach

Cabrera

Gogin

Morland

et al. 2021

2020 25th International Conference on Pattern Recognition (ICPR)

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“…Non-rigid transformation. Demon transformation method introduced in [43] is used in this work as it improves rigid trans-formation in registration accuracy [39]. Specifically, Demon registration method is used to register DW and T2-w images and to generate the most comprehensive details provided by both modalities.…”

Section: ) Non-rigid Registrationmentioning

confidence: 99%

“…However, all these approaches did not exploit DW images where metastatic LNs have a higher intensity than benign ones. In our recent work [39], we used T2-w and DW image fusion step for LN segmentation and classification in cervical cancer MRI. Nevertheless, the segmentation was made by a classic region-growing approach without taking into account the noise and perturbations present in pelvic MRI, PLNs variability as well as the influence of the selection of the initial point.…”

Section: Introductionmentioning

confidence: 99%

Computer-aided Lymph Node Detection using Pelvic Magnetic Resonance Imaging

Bnouni

Rekik

Rhim

et al. 2020

IJCDS

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Pelvic Lymph Nodes (PLNs) segmentation and classification are fundamental tools in the medical image analysis of pelvic gynecological cancer such as endometrial and cervical cancer. Often used by the radiologist, PLN classification requires detailed knowledge of the morphological features of PLNs, derived from size, shape, contour and heterogeneous appearance. Accurate PLN segmentation is an essential step in PLN classification. In order to supply the best assessment of a nodal status, semiautomatic and automatic PLN segmentation and classification methods are highly desired as they can strongly capture the wide variability in morphological features and reduce classification errors due to the inter and intra-observer variability, while avoiding the time-consuming for manual delineation of PLN boundary. Nevertheless, semi-automatic segmentation methods require the clinician intervention to select the initial seed point. However, typical semi-automatic PLN segmentation methods might fail due to (1) the intensity inhomogeneity, noise and low contrast in medical images, and (2) the position of the starting point. Thus, the performance of these methods can be enhanced by using a preprocessing-based iterative segmentation approach. Currently, Magnetic Resonance Imaging (MRI) is the most common imaging modality used for staging endometrial and cervical cancer, evaluation of PLN involvement and selection of therapeutic strategy. PLN detection using classic features can be challenging due to the similarity between normal and abnormal PLNs structures. In pelvic cancer and metastatic PLN, Diffusion Weighted (DW)-MRI exhibits brighter areas indicating tumor and metastatic PLN. This paper combines anatomic T2-Weighted (T2-w) imaging with DW imaging. Specifically, we propose a computer-aided pelvic framework, which leverages (1) an ensemble preprocessing method to improve PLN segmentation, (2) the iterative correction of the position of the initial point by executing the segmentation algorithm several times in succession, (3) the fusion of structural and diffusion MRI and, (4) the extraction of morphological features of segmented PLNs (axial T2-w image) as well as intensity feature derived from the fused image for the final classification of PLNs as suspect or non-suspect. Research in the field of PLN detection is important as it can help doctors to better detect cervical and endometrial cancer and decide the appropriate treatment. To the best of our knowledge, this is the first work to segment and classify PLN. Our preprocessing-based iterative segmentation approach significantly (p<0.05) improved comparison segmentation methods, with a segmentation accuracy boosted from 61.37% for the conventional region-growing algorithm to 66.53% for the proposed method. Furthermore, we obtained an average accuracy of 78.50% for pelvic nodule classification.

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“…T2w-MRI images of the pelvis are acquired in axial, sagittal and coronal planes. In cervical cancer, while axial-T2w images arranged perpendicular to the long axis of cervix yield more precise evaluation of the parametrial invasion, the nodes status [5] and the stromal involvement, sagittal-T2w images planned parallel to the long axis of cervix provide more accurate assessment of the tumor size [6], [7] and the extension of neighboring organs.…”

Section: Introductionmentioning

confidence: 99%

Cross-View Self-Similarity Using Shared Dictionary Learning for Cervical Cancer Staging

et al. 2019

Self Cite

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Dictionary Learning (DL) has gained large popularity in solving different computer vision and medical image problems. However, to the best of our knowledge, it has not been used for cervical tumor staging. More importantly, there have been very limited works on how to aggregate different interactions across data views using DL. As a contribution, we propose a novel cross-view self-similarity low rank shared dictionary learning-based (CVSS-LRSDL) framework, which introduces three major contributions in medical image-based cervical cancer staging: (1) leveraging the complementary of axial and sagittal T2w-magnetic resonance (MR) views for cervical cancer diagnosis, (2) introducing self-similarity (SS) patches for DL training, which explore the unidirectional interaction from a source view to a target one, and (3) extracting features that are shared across tumor grades and grade-specific features using the CVSS-LRSDL learning approach. For the first and second contributions, given an input patch in the source view (axial T2w-MR images), we generate its SS patches within a fixed neighborhood in the target view (sagittal T2w-MR images). Specifically, we produce a unidirectional patch-wise SS from a source to a target view, based on mutual and additional information between both views. As for the third contribution, we represent each individual subject using the weighted distance matrix between views, which is used to train our DL-based classifier to output the label for a new testing subject. Overall, our framework outperformed several DL based multi-label classification methods trained using: (i) patch intensities, (ii) SS single-view patches, and (iii) weighted-SS single-view patches. We evaluated our CVSS-LRSDL framework using 15 T2w-MRI sequences with axial and sagittal views. Our CVSS-LRSDL significantly (p<0.05) outperformed several comparison methods and obtained an average accuracy of 81.73% for cervical cancer staging. INDEX TERMS Shared dictionary learning, low-rank models, self-similarity, cross-view, cervical cancer stage.

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Semi-automatic lymph node segmentation and classification using cervical cancer MR imaging

Cited by 7 publications

References 21 publications

Segmentation of Axillary and Supraclavicular Tumoral Lymph Nodes in PET/CT: A Hybrid CNN/Component-Tree Approach

Segmentation of Axillary and Supraclavicular Tumoral Lymph Nodes in PET/CT: A Hybrid CNN/Component-Tree Approach

Computer-aided Lymph Node Detection using Pelvic Magnetic Resonance Imaging

Cross-View Self-Similarity Using Shared Dictionary Learning for Cervical Cancer Staging

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