Detection of Lymph Nodes in T2 MRI Using Neural Network Ensembles

Mathai, Tejas Sudharshan; Lee, Sungwon; Elton, Daniel C.; Shen, Thomas C.; Peng, Yifan; Lu, Zhiyong; Summers, Ronald M.

doi:10.1007/978-3-030-87589-3_70

Cited by 5 publications

(2 citation statements)

References 33 publications

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“…To alleviate these imaging and workflow related issues, a number of lymph node (LN) detection algorithms have been published in literature. [4][5][6][7][8][9][10] Some of these approaches focus on detecting LNs in specific regions of body (pelvis 6 and rectal 4 areas), while others universally detect both benign and malignant nodes without ILL on a T2FS slice, while (h) shows results with ILL on an interpolated slice. Note that a LN was missed in (g) and (h) that was captured in (d).…”

Section: Introductionmentioning

confidence: 99%

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Universal lymph node detection in multiparametric MRI with selective augmentation

Mathai

Lee

Shen

et al. 2023

Medical Imaging 2023: Computer-Aided Diagnosis

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Robust localization of lymph nodes (LNs) in multiparametric MRI (mpMRI) is critical for the assessment of lymphadenopathy. Radiologists routinely measure the size of LN to distinguish benign from malignant nodes, which would require subsequent cancer staging. Sizing is a cumbersome task compounded by the diverse appearances of LNs in mpMRI, which renders their measurement difficult. Furthermore, smaller and potentially metastatic LNs could be missed during a busy clinical day. To alleviate these imaging and workflow problems, we propose a pipeline to universally detect both benign and metastatic nodes in the body for their ensuing measurement. The recently proposed VFNet neural network was employed to identify LN in T2 fat suppressed and diffusion weighted imaging (DWI) sequences acquired by various scanners with a variety of exam protocols. We also use a selective augmentation technique known as Intra-Label LISA (ILL) to diversify the input data samples the model sees during training, such that it improves its robustness during the evaluation phase. We achieved a sensitivity of ∼83% with ILL vs. ∼80% without ILL at 4 FP/vol. Compared with current LN detection approaches evaluated on mpMRI, we show a sensitivity improvement of ∼9% at 4 FP/vol.

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Section: Introductionmentioning

confidence: 99%

“…in the body. 5,[7][8][9][10] However, very few of these approaches 4,5 focus their attention on using mpMRI for LN detection. Difficulty in obtaining retrospective mpMRI studies with different series for algorithmic development is one potential reason.…”

Section: Introductionmentioning

confidence: 99%

Universal lymph node detection in multiparametric MRI with selective augmentation

Mathai

Lee

Shen

et al. 2023

Medical Imaging 2023: Computer-Aided Diagnosis

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Geometry-Adaptive Network for Robust Detection of Placenta Accreta Spectrum Disorders

Chen,

Zhu,

Shen

et al. 2023

Lecture Notes in Computer Science

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From single to universal: tiny lesion detection in medical imaging

Zhang,

Mao,

et al. 2024

Artif Intell Rev

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Accurate and automatic detection of tiny lesions in medical imaging plays a critical role in comprehensive cancer diagnosis, staging, treatment, follow-up, and prognosis. Numerous deep learning (DL) based studies have focused on single lesions, providing highly sensitive identification and precise localization. On the other hand, some recent studies have started to concentrate on whole-body lesions, as they could provide systemic clinical support. This paper presents a single-to-universal review of DL studies on tiny lesion detection in medical imaging, with a particular emphasis on detection models and techniques, as well as the data-related aspects such as modality, dimension, and dataset. A wide range of tasks are covered, including traditional single lesion detection tasks such as lung nodules, breast masses, thyroid nodules, and diseased lymph nodes, as well as the emerging task of universal lesion detection. Moreover, the paper provides in-depth analysis and discussion, with the hope of inspiring future research endeavors.

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Detection of Lymph Nodes in T2 MRI Using Neural Network Ensembles

Cited by 5 publications

References 33 publications

Universal lymph node detection in multiparametric MRI with selective augmentation

Universal lymph node detection in multiparametric MRI with selective augmentation

Geometry-Adaptive Network for Robust Detection of Placenta Accreta Spectrum Disorders

From single to universal: tiny lesion detection in medical imaging

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