A texture-based 3D region growing approach for segmentation of ICA through the skull base in CTA

Bozkurt, Ferhat; Köse, Cemal; Sarı, Ahmet

doi:10.1007/s11042-020-09690-z

Cited by 5 publications

(4 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These performance metrics are calculated from confusion matrix and mathematically defined as follows. 38 , 39

…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

A deep and handcrafted features‐based framework for diagnosis of COVID‐19 from chest x‐ray images

Bozkurt

2021

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

Automatic early diagnosis of COVID‐19 with computer‐aided tools is crucial for disease treatment and control. Radiology images of COVID‐19 and other lung diseases like bacterial pneumonia, viral pneumonia have common features. Thus, this similarity makes it difficult for radiologists to detect COVID‐19 cases. A reliable method for classifying non‐COVID‐19 and COVID‐19 chest x‐ray images could be useful to reduce triage process and diagnose. In this study, we develop an original framework (HANDEFU) that supports handcrafted, deep, and fusion‐based feature extraction techniques for feature engineering. The user interactively builds any model by selecting feature extraction technique and classification method through the framework. Any feature extraction technique and model could then be added dynamically to the library of software at a later time upon request. The novelty of this study is that image preprocessing and diverse feature extraction and classification techniques are assembled under an original framework. In this study, this framework is utilized for diagnosing COVID‐19 from chest x‐ray images on an open‐access dataset. All of the experimental results and performance evaluations on this dataset are performed with this software. In experimental studies, COVID‐19 prediction is performed by 27 different models through software. The superior performance with accuracy of 99.36% is obtained by LBP+SVM model.

show abstract

“…These performance metrics are calculated from confusion matrix and mathematically defined as follows. 38 , 39

…”

Section: Resultsmentioning

confidence: 99%

“…In this figure; 0, 1, and 2 for row and column name represents (0=COVID‐19), (1= viral pneumonia) and (2=normal) class respectively. These performance metrics are calculated from confusion matrix and mathematically defined as follows 38,39

P R = T P / (T P + F P)

R E = T P / (T P + F N)

F 1 - S C = (2 x T P) / (2 x T P + F N + F P)

A C C = (T P + T N) / (T P + F N + F P + T N)

…”

Section: Resultsmentioning

confidence: 99%

A deep and handcrafted features‐based framework for diagnosis of COVID‐19 from chest x‐ray images

Bozkurt

2021

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

show abstract

“…2a/2b. In order to make vessels stand out better, we introduce the contrast enhanced local intensity fusion (CE-LIF) 3 in Fig. 2c.…”

Section: Local Intensity Fusion: Lifmentioning

confidence: 99%

“…Unlike magnetic resonance angiography (MRA) and computed tomography angiography (CTA), OCT-A suffers from severe speckle noise, which induces poor contrast and vessel discontinuity. Consequently, unsupervised vessel segmentation approaches [3,33,2,22,28] developed for other modalities do not translate well to OCT-A. Denoising OCT/OCT-A images has thus been an active topic of research [24,13,5].…”

Section: Introductionmentioning

confidence: 99%

LIFE: A Generalizable Autodidactic Pipeline for 3D OCT-A Vessel Segmentation

Cui

et al. 2021

Preprint

View full text Add to dashboard Cite

Optical coherence tomography (OCT) is a non-invasive imaging technique widely used for ophthalmology. It can be extended to OCT angiography (OCT-A), which reveals the retinal vasculature with improved contrast. Recent deep learning algorithms produced promising vascular segmentation results; however, 3D retinal vessel segmentation remains difficult due to the lack of manually annotated training data. We propose a learning-based method that is only supervised by a self-synthesized modality named local intensity fusion (LIF). LIF is a capillary-enhanced volume computed directly from the input OCT-A. We then construct the local intensity fusion encoder (LIFE) to map a given OCT-A volume and its LIF counterpart to a shared latent space. The latent space of LIFE has the same dimensions as the input data and it contains features common to both modalities. By binarizing this latent space, we obtain a volumetric vessel segmentation. Our method is evaluated in a human fovea OCT-A and three zebrafish OCT-A volumes with manual labels. It yields a Dice score of 0.7736 on human data and 0.8594 ± 0.0275 on zebrafish data, a dramatic improvement over existing unsupervised algorithms.

show abstract

LIFE: A Generalizable Autodidactic Pipeline for 3D OCT-A Vessel Segmentation

Cui

et al. 2021

Lecture Notes in Computer Science

View full text Add to dashboard Cite

A texture-based 3D region growing approach for segmentation of ICA through the skull base in CTA

Cited by 5 publications

References 58 publications

A deep and handcrafted features‐based framework for diagnosis of COVID‐19 from chest x‐ray images

A deep and handcrafted features‐based framework for diagnosis of COVID‐19 from chest x‐ray images

LIFE: A Generalizable Autodidactic Pipeline for 3D OCT-A Vessel Segmentation

LIFE: A Generalizable Autodidactic Pipeline for 3D OCT-A Vessel Segmentation

Contact Info

Product

Resources

About