Multiple Tissue Sample Collection Device for MRI Guided Transrectal Prostate Biopsy: Optimization and MRI Compatibility Tests

Anique, Farrukh; Liu, Rongrong; Farooq, Muhammad Umar; Oh, Se Baek; Jo, Jung Ki; Ko, Seong Young

doi:10.1109/access.2023.3293735

IEEE Access

2023

DOI: 10.1109/access.2023.3293735

|View full text |Cite

Multiple Tissue Sample Collection Device for MRI Guided Transrectal Prostate Biopsy: Optimization and MRI Compatibility Tests

Farrukh Anique

Rongrong Liu

Muhammad Umar Farooq

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2024

Publication Types

Select...

Article2

Relationship

Self Cite1

Independent1

Authors

Journals

Cited by 2 publications

(2 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rapid segmentation of animal organs is beneficial for evaluating the performance of medical devices. In addition, our research group is developing key components for a fully automatic biopsy robotic system in an MRI environment [2][3][4]. Evaluating biopsy sampling performance requires localization of the animal prostate.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Cross‐Species Segmentation of Animal Prostate Using a Human Prostate Dataset and Limited Preoperative Animal Images: A Sampled Experiment on Dog Prostate Tissue

Yang,

2024

Int J Imaging Syst Tech

Self Cite

View full text Add to dashboard Cite

In the development of medical devices and surgical robot systems, animal models are often used for evaluation, necessitating accurate organ segmentation. Deep learning‐based image segmentation provides a solution for automatic and precise organ segmentation. However, a significant challenge in this approach arises from the limited availability of training data for animal models. In contrast, human medical image datasets are readily available. To address this imbalance, this study proposes a fine‐tuning approach that combines a limited set of animal model images with a comprehensive human image dataset. Various postprocessing algorithms were applied to ensure that the segmentation results met the positioning requirements for the evaluation of a medical robot under development. As one of the target applications, magnetic resonance images were used to determine the position of the dog's prostate, which was then used to determine the target location of the robot under development. The MSD TASK5 dataset was used as the human dataset for pretraining, which involved a modified U‐Net network. Ninety‐nine pretrained backbone networks were tested as encoders for U‐Net. The cross‐training validation was performed using the selected network backbone. The highest accuracy, with an IoU score of 0.949, was achieved using the independent validation set from the MSD TASK5 human dataset. Subsequently, fine‐tuning was performed using a small set of dog prostate images, resulting in the highest accuracy of an IoU score of 0.961 across different cross‐validation groups. The processed results demonstrate the feasibility of the proposed approach for accurate prostate segmentation.

show abstract

mentioning

confidence: 99%

“…This paper presents a case study involving automatic localization of the dog's prostate during the development of an MRIcompatible prostate biopsy robotic system [2][3][4]. For the human prostate dataset, we used the MSD TASK5 dataset [5], which is comprehensive and openly available.…”

mentioning

confidence: 99%

Cross‐Species Segmentation of Animal Prostate Using a Human Prostate Dataset and Limited Preoperative Animal Images: A Sampled Experiment on Dog Prostate Tissue

Yang,

2024

Int J Imaging Syst Tech

Self Cite

View full text Add to dashboard Cite

show abstract

IVNet: Transfer Learning Based Diagnosis of Breast Cancer Grading Using Histopathological Images of Infected Cells

Aziz,

Munir,

Raza

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Breast cancer constitutes a significant global health concern that impacts millions of women across the world. The diagnosis of breast cancer involves categorizing grades based on the histopathological characteristics of tumor cells. While histopathological assessment remains the established benchmark for breast cancer diagnosis, it is hampered by time-consuming procedures, subjectivity, and susceptibility to human errors. This study introduces a novel approach called ImageNet-VGG16 (IVNet) for the realtime diagnosis of breast cancer within a hospital environment. The research experiments are conducted using a benchmark dataset known as Jimma University Medical Center (JUMC) breast cancer grading. Advanced image processing techniques are applied to preprocess the data, enhancing performance. This preprocessing involves the utilization of Holistically Nested Edge Detection (HED) and Contrast Limited Adaptive Histogram Equalization (CLAHE) for transformation and stain normalization. We employ advanced neural network-based transfer learning techniques to analyze the preprocessed histopathological images and identify affected cells. Various pre-trained models are utilized, including convolutional neural networks (CNN) such as VGG16, ResNet50, InceptionNetv3, ImageNet, MobileNetv3, and EfficientNetV3, in a comparative framework. The principal objective of this research is the accurate classification of breast cancer images into Grade-1, Grade-2 and Grade-3. Through extensive experimental research, we achieved a commendable 97% correct classification rate by utilizing a hybrid of VGG16 and ImageNet as the proposed feature engineering method, IVNet. We also validate our proposed approach performance using other stateof-the-art study data and statistical t-test analysis. Furthermore, we develop a user-friendly Graphical User Interface (GUI) that facilitates real-time cell tracking in histopathological images. Our real-time diagnosing application offers valuable insights for treatment planning and assists medical professionals in making prognoses. Moreover, our approach can serve as a reliable decision support system for pathologists and clinicians, particularly in settings constrained by limited resources and restricted access to expertise and equipment.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Multiple Tissue Sample Collection Device for MRI Guided Transrectal Prostate Biopsy: Optimization and MRI Compatibility Tests

Cited by 2 publications

References 47 publications

Cross‐Species Segmentation of Animal Prostate Using a Human Prostate Dataset and Limited Preoperative Animal Images: A Sampled Experiment on Dog Prostate Tissue

Cross‐Species Segmentation of Animal Prostate Using a Human Prostate Dataset and Limited Preoperative Animal Images: A Sampled Experiment on Dog Prostate Tissue

IVNet: Transfer Learning Based Diagnosis of Breast Cancer Grading Using Histopathological Images of Infected Cells

Contact Info

Product

Resources

About