Automatic Segmentation of the Cerebral Falx and Adjacent Gyri in 2D Ultrasound Images

Nitsch, Jennifer; Klein, Jan; Miller, Dorothea; Sure, Ulrich; Hahn, Horst K.

doi:10.1007/978-3-662-46224-9_50

Cited by 4 publications

(5 citation statements)

References 4 publications

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“…In both outlined cases, the segmented structures need to be clearly visible from most angles and even after brain shift has occurred. As described in a previous work by our group [18], it can be useful to use lateral ventricles, but also rather superficial structures such as sulci, mainly the prominent central and precentral sulcus and of course as already shown by Nitsch et al [16] the cerebellar tentorium, and the falx cerebri which we expand towards the transverse and longitudinal fissure, respectively. The latter is especially useful when battling brain shift, since only minor shift in the midline is reported [2,7].…”

Section: Introductionmentioning

confidence: 70%

“…However, usually only cystic tumors manifest as hyperechogenic in ultrasound, therefore this method is not generalizable. As already described above, Nitsch et al [16] segment falx cerebri and its neighboring gyri in 2D ultrasound with the goal to facilitate registration with pre-operative MRI. This method is also not generally applicable, since the segmented structure is only visible in certain surgical settings.…”

Section: State-of-the-artmentioning

confidence: 99%

“…Therefore, any manual interaction like landmark selection or feature extraction is prone to error, which is reflected in rather high reported inter-and intra-observer variability of up to 1.6 mm [13]. Instead, it can be advantageous to segment correlating structures in both modalities that can then be used for initialization [18] or even registration [16].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fully Automatic Segmentation of 3D Brain Ultrasound: Learning from Coarse Annotations

Rackerseder¹,

Göbl²,

Navab³

et al. 2019

Preprint

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Intra-operative ultrasound is an increasingly important imaging modality in neurosurgery. However, manual interaction with imaging data during the procedures, for example to select landmarks or perform segmentation, is difficult and can be time consuming. Yet, as registration to other imaging modalities is required in most cases, some annotation is necessary. We propose a segmentation method based on DeepVNet and specifically evaluate the integration of pre-training with simulated ultrasound sweeps to improve automatic segmentation and enable a fully automatic initialization of registration. In this view, we show that despite training on coarse and incomplete semi-automatic annotations, our approach is able to capture the desired superficial structures such as sulci, the cerebellar tentorium, and the falx cerebri. We perform a five-fold cross-validation on the publicly available RESECT dataset. Trained on the dataset alone, we report a Dice and Jaccard coefficient of 0.45 ± 0.09 and 0.30 ± 0.07 respectively, as well as an average distance of 0.78 ± 0.36 mm. With the suggested pre-training, we computed a Dice and Jaccard coefficient of 0.47 ± 0.10 and 0.31 ± 0.08, and an average distance of 0.71 ± 0.38 mm. The qualitative evaluation suggest that with pre-training the network can learn to generalize better and provide refined and more complete segmentations in comparison to incomplete annotations provided as input.

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Section: Introductionmentioning

confidence: 70%

Section: State-of-the-artmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fully Automatic Segmentation of 3D Brain Ultrasound: Learning from Coarse Annotations

Rackerseder¹,

Göbl²,

Navab³

et al. 2019

Preprint

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“…This paper focused on the segmentation of HE regions like falx Cerebri and tentorium cerebelli as a prework for the registration of intra-operative US to the preoperative MR images. US image slices are prepossessed with two stick filters as described in [64]. The stick filters strengthen and sustain the line-type structures and perform smoothing of the homogeneous regions by reducing speckle.…”

Section: Resultsmentioning

confidence: 99%

A Fast Level-Set Curve-Flow Formulation by Logarithmic Step-Size Control for Brain Ultrasound Image Segmentation

Chel,

Bora,

Ramchiary

2023

IEEE Access

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Segmentation of different regions in intra-operative brain ultrasound (iBUS) images is often required for assiting the neuro-surgeon. Traditional level-set and active contour-based semi-automatic image segmentation approaches suffer from low accuracy and slow convergence. This paper presents a novel semiautomatic level-set approach for segmenting hyper-echoic (HE), hypo-echoic, and anechoic regions with minimal user intervention. Three hyper-echoic regions longitudinal fissure, choroid plexus, and tumor and two anechoic regions, namely ventricle and resection cavity are segmented using a patch-based level-set approach. This method is a combination of three procedures: a) level-set based unidirectional level-set curve flow(ULSCF), b) bidirectional level-set curve flow(BLSCF) using a logarithmic patch size control, and c) cubic B-spline-based contour smoothing. Zero level-set curve is derived using a patch-based intensity thresholding method of the desired region. The imperfection on the blocky edges produced during the patchbased ULSCF, are minimized using the BLSCF step that uses local region splitting approach. Slope and curvature discontinuities of the resulting boundaries after BLSCF are eliminated using a cubic B-spline based contour smoothing. The performance of the proposed method outperforms other state-of-the-art levelset and active contour methods, and the desired result is obtained within reasonable time required for online monitoring during surgery.

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“…At first, the Hessian filtering was applied. The reason to choose this filtering method is, that it can detect the local strength of lines, ridges and direction of edges [4,5,9]. In the next step to the analyzed image a binarization is applied using the Otsu method -a well known binarization technique.…”

Section: Knuckle Analysis Subsystemmentioning

confidence: 99%

Keystroke Dynamics and Finger Knuckle Imaging Fusion for Continuous User Verification

Wesołowski

Doroz

Wróbel

et al. 2017

Computer Information Systems and Industrial Management

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Part 2: Biometrics and Pattern Recognition ApplicationsInternational audienceThe paper presents a novel user identity verification method based on fusion of keystroke dynamics and knuckle images analysis. In our solution the verification is performed by an ensemble of classifiers used to verify the identity of an active user. A proposed verification module works on a database which comprises of data representing keystroke dynamics and knuckle images. The usability of the introduced approach was tested experimentally. The obtained results confirm that the proposed fusion method gives better results than the use of a single biometric feature only. For this reason our method can be used for increasing a protection level of computer resources against impostors. The paper presents preliminary research conducted to assess the potential of biometric methods fusion

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Automatic Segmentation of the Cerebral Falx and Adjacent Gyri in 2D Ultrasound Images

Cited by 4 publications

References 4 publications

Fully Automatic Segmentation of 3D Brain Ultrasound: Learning from Coarse Annotations

Fully Automatic Segmentation of 3D Brain Ultrasound: Learning from Coarse Annotations

A Fast Level-Set Curve-Flow Formulation by Logarithmic Step-Size Control for Brain Ultrasound Image Segmentation

Keystroke Dynamics and Finger Knuckle Imaging Fusion for Continuous User Verification

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