Multiple Object Tracking for Occluded Particles

Qian, Yifei; Ji, Ren; Duan, Yuping; Yang, Runhuai

doi:10.1109/access.2020.3047099

Cited by 1 publication

(2 citation statements)

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“…Their evaluation's findings demonstrated that the mixed algorithm was effective in identifying lesions with high accuracy, giving the exact anatomical location of the stenosis. Even though the study was done only using inputs from time-of-flight magnetic resonance angiography (TOF-MRA) scanner images, the proposed method showed favourable results in all three accuracy measurements -precision, recall, and mAP with intersection over union (IoU) value at 0.5 -when compared other commonly used algorithms [29]. As the author mentioned, the proposed method lacks additional inputs and assessment from other scanner types than TOF-MRA to measure its limitations.…”

Section: Occluded Object Detection In Medical Imagingmentioning

confidence: 99%

“…One recent advancement is the "Global Hungarian algorithm". The two-step recognition algorithm presented by Qian et al, [29] involves threshold partitioning and morphological open operation that utilizes the advantages from the global shortest path and Hungarian algorithm. Said method was employed and tested on occluded non-fluorescent labelled microbe particles.…”

Section: Occluded Object Detection In Medical Imagingmentioning

confidence: 99%

See 1 more Smart Citation

A Review on Occluded Object Detection and Deep Learning-Based Approach in Medical Imaging-Related Research

Khairul Rohaizzat Jamaluddin,

Shafaf Ibrahim

2023

ARASET

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Medical image occlusions can arise due to several factors, including anatomical features, imaging modality, and acquisition settings. Object occlusion occurrence has a very significant effect on detection accuracy in general and in medical cases, these effects hinder proper diagnosis and treatment plans that may be fatal for the patients. Thus, precise occluded object detection is imperative. This paper aims to review the various state-of-the-art models and approaches that had been proposed for the occluded object detection. The coverage of this paper includes occluded object detection models in other applications and medical imaging, its proposed Deep Learning implementations, hybrid Deep Learning, and statistical analysis that were integrated into Deep Learning models. It is found that, in overall, more annotated medical image datasets are required to reduce overfitting occurrence, numerous Deep Learning models and its hybrid combination’s applications yet to be tested of its limitations, and the extent of statistical analysis integration on Deep Learning models.

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Section: Occluded Object Detection In Medical Imagingmentioning

confidence: 99%

Section: Occluded Object Detection In Medical Imagingmentioning

confidence: 99%