A New Approach of Skull Fracture Detection in CT Brain Images

Zaki, Wan Mimi Diyana Wan; Fauzi, Mohammad Faizal Ahmad; Besar, Rosli

doi:10.1007/978-3-642-05036-7_16

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…These studies mostly used computer vision techniques, such as the black-hat transform, to detect fractures on the skull from head CT scans [11], [12]. However, the computer vision techniques were unreliable due to the variety and complexity of the fractures that appear on the skull.…”

Section: Related Workmentioning

confidence: 99%

Automated Detection of Craniomaxillofacial Fractures From 3D CT Images Using Ensemble Deep Learning-Based Segmentation Models

Rachmadi,

Kreshanti,

Anggraeni

et al. 2024

Preprint

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Section: Related Workmentioning

confidence: 99%

Automated Detection of Craniomaxillofacial Fractures From 3D CT Images Using Ensemble Deep Learning-Based Segmentation Models

Rachmadi,

Kreshanti,

Anggraeni

et al. 2024

Preprint

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“…utilized the number of closed areas formed by edges to recognize skull fractures. Later, the number of skeleton endpoints was applied for SFD by the same research team 5 . Abubacker et al 6 .…”

Section: Introductionmentioning

confidence: 99%

“…Later, the number of skeleton endpoints was applied for SFD by the same research team. 5 Abubacker et al 6 detected skull fractures by the disconnectedness of adjacent pixels. Yamada et al 7 leveraged a black-hat transformation to identify skull fractures.…”

Section: Introductionmentioning

confidence: 99%

Fracture R‐CNN: An anchor‐efficient anti‐interference framework for skull fracture detection in CT images

et al. 2022

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Background Skull fracture, as a common traumatic brain injury, can lead to multiple complications including bleeding, leaking of cerebrospinal fluid, infection, and seizures. Automatic skull fracture detection (SFD) is of great importance, especially in emergency medicine. Purpose Existing algorithms for SFD, developed based on hand‐crafted features, suffer from low detection accuracy due to poor generalizability to unseen samples. Deploying deep detectors designed for natural images like Faster Region‐based Convolutional Neural Network (R‐CNN) for SFD can be helpful but are of high redundancy and with nonnegligible false detections due to the cranial suture and skull base interference. Therefore, we, for the first time, propose an anchor‐efficient anti‐interference deep learning framework named Fracture R‐CNN for accurate SFD with low computational cost. Methods The proposed Fracture R‐CNN is developed by incorporating the prior knowledge utilized in clinical diagnosis into the original Faster R‐CNN. Specifically, based on the distributions of skull fractures, we first propose an adaptive anchoring region proposal network (AA‐RPN) to generate proposals for diverse‐scale fractures with low computational complexity. Then, based on the prior knowledge that cranial sutures exist in the junctions of bones and usually contain sclerotic margins, we design an anti‐interference head (A‐Head) network to eliminate the cranial suture interference for better SFD detection. In addition, to further enhance the anti‐interference ability of the proposed A‐Head, a difficulty‐balanced weighted loss function is proposed to emphasize more on distinguishing the interference areas from the skull base and the cranial sutures during training. Results Experimental results demonstrate that the proposed Fracture R‐CNN outperforms the current state‐of‐the‐art (SOTA) deep detectors for SFD with a higher recall and fewer false detections. Compared to Faster R‐CNN, the proposed Fracture R‐CNN improves the average precision (AP) by 11.74% and the free‐response receiver operating characteristic (FROC) score by 11.08%. Through validating on various backbones, we further demonstrate the architecture independence of Fracture R‐CNN, making it extendable to other detection applications. Conclusions As the customized deep learning–based framework for SFD, Fracture R‐CNN can effectively overcome the unique challenges in SFD with less computational cost, leading to a better detection performance compared to the SOTA deep detectors. Moreover, we believe the prior knowledge explored for Fracture R‐CNN would shed new light on future deep learning approaches for SFD.

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Automated skull damage detection from assembled skull model using computer vision and machine learning

Mangrulkar¹,

Rane²,

Sunnapwar³

2021

Int. j. inf. tecnol.

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A New Approach of Skull Fracture Detection in CT Brain Images

Cited by 5 publications

References 8 publications

Automated Detection of Craniomaxillofacial Fractures From 3D CT Images Using Ensemble Deep Learning-Based Segmentation Models

Automated Detection of Craniomaxillofacial Fractures From 3D CT Images Using Ensemble Deep Learning-Based Segmentation Models

Fracture R‐CNN: An anchor‐efficient anti‐interference framework for skull fracture detection in CT images

Automated skull damage detection from assembled skull model using computer vision and machine learning

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