BiFTransNet: A unified and simultaneous segmentation network for gastrointestinal images of CT &amp; MRI

Jiang, Xin; Ding, Yizhou; Liu, Mingzhe; Wang, Yong; Li, Yan; Wu, Zongda

doi:10.1016/j.compbiomed.2023.107326

Cited by 22 publications

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, accelerated deployment could be expedited through automated tumor segmentation—several such promising tools have recently been developed for vestibular schwannoma, however, in all cases the authors acknowledge that these will require further validation before implementation 45 – 48 . This approach has shown significant promise in other medical contexts, particularly in developing strategies for automating chest X-ray review during the COVID-19 pandemic 49 , 50 , and in the identification of concerning vs. benign gastrointestinal polyps 51 , 52 . Lastly, as data science techniques are increasingly applied in medicine, no discussion of their implementation in this context is complete without considering the protection of patient privacy and confidentiality.…”

Section: Discussionmentioning

confidence: 99%

A multi-institutional machine learning algorithm for prognosticating facial nerve injury following microsurgical resection of vestibular schwannoma

Heman-Ackah,

Blue,

Quimby

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Vestibular schwannomas (VS) are the most common tumor of the skull base with available treatment options that carry a risk of iatrogenic injury to the facial nerve, which can significantly impact patients’ quality of life. As facial nerve outcomes remain challenging to prognosticate, we endeavored to utilize machine learning to decipher predictive factors relevant to facial nerve outcomes following microsurgical resection of VS. A database of patient-, tumor- and surgery-specific features was constructed via retrospective chart review of 242 consecutive patients who underwent microsurgical resection of VS over a 7-year study period. This database was then used to train non-linear supervised machine learning classifiers to predict facial nerve preservation, defined as House-Brackmann (HB) I vs. facial nerve injury, defined as HB II–VI, as determined at 6-month outpatient follow-up. A random forest algorithm demonstrated 90.5% accuracy, 90% sensitivity and 90% specificity in facial nerve injury prognostication. A random variable (rv) was generated by randomly sampling a Gaussian distribution and used as a benchmark to compare the predictiveness of other features. This analysis revealed age, body mass index (BMI), case length and the tumor dimension representing tumor growth towards the brainstem as prognosticators of facial nerve injury. When validated via prospective assessment of facial nerve injury risk, this model demonstrated 84% accuracy. Here, we describe the development of a machine learning algorithm to predict the likelihood of facial nerve injury following microsurgical resection of VS. In addition to serving as a clinically applicable tool, this highlights the potential of machine learning to reveal non-linear relationships between variables which may have clinical value in prognostication of outcomes for high-risk surgical procedures.

show abstract

Section: Discussionmentioning

confidence: 99%

A multi-institutional machine learning algorithm for prognosticating facial nerve injury following microsurgical resection of vestibular schwannoma

Heman-Ackah,

Blue,

Quimby

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Overall, the network extracts relevant feature sets to generate accurate segmentation of organs such as kidneys by combining a deep multiresolution residual network and nested block (SA) self-attention to take advantage of multiscale features and self-attention mechanisms. To address the limitations in global and local information feature fusion in the classical TransUnet model decoder, Jiang et al [ 67 ] proposed BiFTransNet, which introduces the BiFusion module into the decoder stage to achieve effective global and local feature fusion by enabling feature integration from various modules. It is used in the Synapse dataset to develop automated gastrointestinal image segmentation to help radiation oncologists accurately target the X-ray beam to the tumor.…”

Section: Application Of Transformer In Renal Image Processingmentioning

confidence: 99%

Application of visual transformer in renal image analysis

Yin,

Tang,

Weng

2024

BioMed Eng OnLine

View full text Add to dashboard Cite

Deep Self-Attention Network (Transformer) is an encoder–decoder architectural model that excels in establishing long-distance dependencies and is first applied in natural language processing. Due to its complementary nature with the inductive bias of convolutional neural network (CNN), Transformer has been gradually applied to medical image processing, including kidney image processing. It has become a hot research topic in recent years. To further explore new ideas and directions in the field of renal image processing, this paper outlines the characteristics of the Transformer network model and summarizes the application of the Transformer-based model in renal image segmentation, classification, detection, electronic medical records, and decision-making systems, and compared with CNN-based renal image processing algorithm, analyzing the advantages and disadvantages of this technique in renal image processing. In addition, this paper gives an outlook on the development trend of Transformer in renal image processing, which provides a valuable reference for a lot of renal image analysis.

show abstract

“…If someone suffers an ACL injury, the most common method to diagnose it is performing a magnetic resonance imaging (MRI) scan on the knee joint 15 – 17 . This diagnostic test produces a highly detailed image of the interior constructions of the knee, which is then carefully examined by a radiologist or other trained medical professional 18 .…”

Section: Introductionmentioning

confidence: 99%

Improving inceptionV4 model based on fractional-order snow leopard optimization algorithm for diagnosing of ACL tears

Wang,

Yan

2024

Sci Rep

View full text Add to dashboard Cite

In the current research study, a new method is presented to diagnose Anterior Cruciate Ligament (ACL) tears by introducing an optimized version of the InceptionV4 model. Our proposed methodology utilizes a custom-made variant of the Snow Leopard Optimization Algorithm, known as the Fractional-order Snow Leopard Optimization Algorithm (FO-LOA), to extract essential features from knee magnetic resonance imaging (MRI) images. This results in a substantial improvement in the accuracy of ACL tear detection. By effectively extracting critical features from knee MRI images, our proposed methodology significantly enhances diagnostic accuracy, potentially reducing false negatives and false positives. The enhanced model based on FO-LOA underwent thorough testing using the MRNet dataset, demonstrating exceptional performance metrics including an accuracy rate of 98.00%, sensitivity of 98.00%, precision of 97.00%, specificity of 98.00%, F1-score of 98.00%, and Matthews Correlation Coefficient (MCC) of 88.00%. These findings surpass current methodologies like Convolutional Neural Network (CNN), Inception-v3, Deep Belief Networks and Improved Honey Badger Algorithm (DBN/IHBA), integration of the CNN with an Amended Cooking Training-based Optimizer version (CNN/ACTO), Self-Supervised Representation Learning (SSRL), signifying a significant breakthrough in ACL injury diagnosis. Using FO-SLO to optimize the InceptionV4 framework shows promise in improving the accuracy of ACL tear identification, enabling prompt and efficient treatment interventions.

show abstract

BiFTransNet: A unified and simultaneous segmentation network for gastrointestinal images of CT & MRI

Cited by 22 publications

References 20 publications

A multi-institutional machine learning algorithm for prognosticating facial nerve injury following microsurgical resection of vestibular schwannoma

A multi-institutional machine learning algorithm for prognosticating facial nerve injury following microsurgical resection of vestibular schwannoma

Application of visual transformer in renal image analysis

Improving inceptionV4 model based on fractional-order snow leopard optimization algorithm for diagnosing of ACL tears

Contact Info

Product

Resources

About