Fast and Robust 3-D MRI Brain Structure Segmentation

Wels, Michael; Zheng, Yefeng; Carneiro, Gustavo; Huber, Martin; Hornegger, Joachim; Comanicìu, Dorin

doi:10.1007/978-3-642-04271-3_70

Cited by 15 publications

(14 citation statements)

References 12 publications

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“…The first model in the detector exploits a limited set of features which includes low-level 3D Haar-like features. The other subsequent two models exploit the full heterogeneous set of features, which are of different nature and describe various characteristics of the suspected lesion [13, 15, 16]. The lesion centre detector cascade provides rough to coarse lesion detection, which starts with a large set of suspicious lesion-like structures and ends with a reduced set of likely malignant clinically important findings.…”

Section: Methodsmentioning

confidence: 99%

Automatic detection of lytic and blastic thoracolumbar spine metastases on computed tomography

et al. 2013

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ObjectiveTo evaluate a computer-aided detection (CADe) system for lytic and blastic spinal metastases on computed tomography (CT).MethodsWe retrospectively evaluated the CADe system on 20 consecutive patients with 42 lytic and on 30 consecutive patients with 172 blastic metastases. The CADe system was trained using CT images of 114 subjects with 102 lytic and 308 blastic spinal metastases. Lesions were annotated by experienced radiologists. Detected benign lesions were considered false-positive findings. Detector sensitivity and the number of false-positive findings were calculated as the criteria for detector performance, and free-response receiver operating characteristic (FROC) analysis was conducted. Detailed analysis of false-positive and false-negative findings was performed.ResultsAlgorithm runtime is 3 ± 0.5 min per patient. The system achieves a sensitivity of 83 % at 3.5 false positives per patient on average for blastic metastases and a sensitivity of 88 % at 3.7 false positives for lytic metastases. False positives appeared predominantly in the area of degenerative changes in the case of the blastic metastasis detector and in osteoporotic areas in the case of the lytic metastasis detector.ConclusionThe CADe system reliably detects thoracolumbar spine metastases in real time. An additional study is planned to evaluate how the bone lesion CADe system improves radiologists’ accuracy and efficiency in a clinical setting.Key Points• Computer-aided detection (CADe) of bone metastases has been developed for spinal CT.• The CADe system exhibits high sensitivity with a tolerable false-positive rate.• Analysis of false-positive detection may further improve the system.• CADe may reduce the number of missed spinal metastases at CT interpretation.

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Section: Methodsmentioning

confidence: 99%

Automatic detection of lytic and blastic thoracolumbar spine metastases on computed tomography

et al. 2013

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“…10 As lesion candidates located close to each other are likely to represent the same underlying osteolytic bone lesion, these redundant detection results are removed by agglomerative clustering. Also, based on the results of the initial vertebral body detector and a graph cut-based segmentation 19 of the vertebral bodies themselves a rough patient-specific estimation of the spongiosa's intensity distribution within all segmented vertebral bodies is used to additionally reject osteolytic lesion candidates whose center voxels are not sufficiently darker than the immediately surrounding spongious bone tissue.…”

Section: Post-processingmentioning

confidence: 99%

Multi-stage osteolytic spinal bone lesion detection from CT data with internal sensitivity control

et al. 2012

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Spinal bone lesion detection is a challenging and important task in cancer diagnosis and treatment monitoring. In this paper we present a method for fully-automatic osteolytic spinal bone lesion detection from 3D CT data. It is a multi-stage approach subsequently applying multiple discriminative models, i.e., multiple random forests, for lesion candidate detection and rejection to an input volume. For each detection stage an internal control mechanism ensures maintaining sensitivity on unseen true positive lesion candidates during training. This way a pre-defined target sensitivity score of the overall system can be taken into account at the time of model generation. For a lesion not only the center is detected but also, during post-processing, its spatial extension along the three spatial axes defined by the surrounding vertebral body's local coordinate system. Our method achieves a cross-validated sensitivity score of 75% and a mean false positive rate of 3.0 per volume on a data collection consisting of 34 patients with 105 osteolytic spinal bone lesions. The median sensitivity score is 86% at 2.0 false positives per volume.

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“…Finally, a highdimensional feature vector suitable for machine learning-based shape prediction is obtained by composing the distances between every individual surface point and the nipple. Here, the nipple position is automatically determined by a machine learning-based landmark detection algorithm using 3D Haar-like features [7,8]. It has been rapidly prototyped using an Integrated Detection Network (IDN) [9].…”

Section: Input Data and Feature Extractionmentioning

confidence: 99%

Data-Driven Breast Decompression and Lesion Mapping from Digital Breast Tomosynthesis

Wels

Kelm

Hammon

et al. 2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

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Abstract. Digital Breast Tomosynthesis (DBT) emerges as a new 3D modality for breast cancer screening and diagnosis. Like in conventional 2D mammography the breast is scanned in a compressed state. For orientation during surgical planning, e.g., during presurgical ultrasoundguided anchor-wire marking, as well as for improving communication between radiologists and surgeons it is desirable to estimate an uncompressed model of the acquired breast along with a spatial mapping that allows localizing lesions marked in DBT in the uncompressed model. We therefore propose a method for 3D breast decompression and associated lesion mapping from 3D DBT data. The method is entirely data-driven and employs machine learning methods to predict the shape of the uncompressed breast from a DBT input volume. For this purpose a shape space has been constructed from manually annotated uncompressed breast surfaces and shape parameters are predicted by multiple multi-variate Random Forest regression. By exploiting point correspondences between the compressed and uncompressed breasts, lesions identified in DBT can be mapped to approximately corresponding locations in the uncompressed breast model. To this end, a thin-plate spline mapping is employed. Our method features a novel completely datadriven approach to breast shape prediction that does not necessitate prior knowledge about biomechanical properties and parameters of the breast tissue. Instead, a particular deformation behavior (decompression) is learned from annotated shape pairs, compressed and uncompressed, which are obtained from DBT and magnetic resonance image volumes, respectively. On average, shape prediction takes 26 s and achieves a surface distance of 15.80±4.70 mm. The mean localization error for lesion mapping is 22.48±8.67 mm.

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Fast and Robust 3-D MRI Brain Structure Segmentation

Cited by 15 publications

References 12 publications

Automatic detection of lytic and blastic thoracolumbar spine metastases on computed tomography

Automatic detection of lytic and blastic thoracolumbar spine metastases on computed tomography

Multi-stage osteolytic spinal bone lesion detection from CT data with internal sensitivity control

Data-Driven Breast Decompression and Lesion Mapping from Digital Breast Tomosynthesis

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