Registration of prostate histology images to ex vivo MR images via strand‐shaped fiducials

Gibson, Eli; Crukley, Cathie; Gaed, Mena; Gómez, José A.; Moussa, Madeleine; Chin, Joseph L.; Bauman, Glenn; Fenster, Aaron; Ward, Aaron D.

doi:10.1002/jmri.23767

Cited by 58 publications

(59 citation statements)

References 36 publications

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“…In this approach, predicted particles are first sampled from the prior distribution, and then propagated towards higher likelihood states based on observations via an update stage, which can be interpreted as an importance sampling of the particles . The weights are recursively updated by (8) After executing the alignment process in Section III-C above, we generate particles according to , i.e., state vectors that equal to the initial state. We set the particles' weights uniformly to and proceed to time step .…”

Section: Particle Filtering Algorithmmentioning

confidence: 99%

“…After all particles are updated, we assign an importance weight to each one, based on (8). To this end, we calculate the prior and likelihood for each particle using (5) and (6), respectively.…”

Section: Particle Filtering Algorithmmentioning

confidence: 99%

“…The works [6], [7] involve placement of fiducial markers into the ex vivo specimen, in order to guide its sectioning along the imaging plane with a magnetically tracked probe, while [8] employs such markers to fit affine transformations between histological slices and the specimen. These methods achieve good registration results, but require a special setup, and additional clinical time and approvals that may not be available in practice.…”

Section: B Related Workmentioning

confidence: 99%

“…In summary, the approaches proposed in the literature typically require special hardware [4], [5], involve insertion of markers to the specimen [3], [6]- [8], are 2-D in nature [12] or assume a dense sectioning for a 3-D registration [13], do not take into account all degrees-of-freedom and/or do not utilize their interdependence, are sensitive to segmentation errors, and/or are prone to local extrema [14]. While fusing several methods and running them multiple times with different initializations may allow for an accurate registration, it would result in a cumbersome and long process that might not be suitable in some scenarios.…”

Section: B Related Workmentioning

confidence: 99%

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Registration of Whole-Mount Histology and Volumetric Imaging of the Prostate Using Particle Filtering

Nir

Sahebjavaher

Kozłowski

et al. 2014

IEEE Trans. Med. Imaging

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Registration of histological slices to volumetric imaging of the prostate is an important task that can be used to optimize imaging for cancer detection. Such registration is challenging due to physical changes of the specimen during excision and fixation, and misalignment of the histological slices during preparation and digital scanning. In this work, we consider a multi-slice to volume registration method in which a stack of sparse, unaligned 2-D whole-mount histological slices is registered to a 3-D volumetric imaging of the prostate. We propose a particle filtering framework to contend with the high dimensionality of the search space and multimodal nature of the optimization. Such framework allows modeling of the uncertainty in the pose of the slices and in the imaged information, in order to derive optimal registration parameters in a Bayesian approach. Intensity-, region-, and point-based similarity metrics were incorporated into the registration algorithm to account for different imaging modalities. We demonstrate and evaluate our method on a diverse set of data that includes a synthetic volume, ex vivo and in vivo magnetic resonance imaging, and in vivo ultrasound.

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Section: Particle Filtering Algorithmmentioning

confidence: 99%

Section: Particle Filtering Algorithmmentioning

confidence: 99%

Section: B Related Workmentioning

confidence: 99%

Section: B Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Registration of Whole-Mount Histology and Volumetric Imaging of the Prostate Using Particle Filtering

Nir

Sahebjavaher

Kozłowski

et al. 2014

IEEE Trans. Med. Imaging

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show abstract

“…Our reference standard for accuracy is based on a rigorous, high-accuracy 3D histology reconstruction and registration to in vivo MRI [11], corresponding the MRI with reference standard histology tumour volumes.…”

Section: Introductionmentioning

confidence: 99%

Accuracy and variability of tumor burden measurement on multi-parametric MRI

et al. 2014

Self Cite

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Measurement of prostate tumour volume can inform prognosis and treatment selection, including an assessment of the suitability and feasibility of focal therapy, which can potentially spare patients the deleterious side effects of radical treatment. Prostate biopsy is the clinical standard for diagnosis but provides limited information regarding tumour volume due to sparse tissue sampling. A non-invasive means for accurate determination of tumour burden could be of clinical value and an important step toward reduction of overtreatment. Multi-parametric magnetic resonance imaging (MPMRI) is showing promise for prostate cancer diagnosis. However, the accuracy and inter-observer variability of prostate tumour volume estimation based on separate expert contouring of T2-weighted (T2W), dynamic contrastenhanced (DCE), and diffusion-weighted (DW) MRI sequences acquired using an endorectal coil at 3T is currently unknown. We investigated this question using a histologic reference standard based on a highly accurate MPMRIhistology image registration and a smooth interpolation of planimetric tumour measurements on histology. Our results showed that prostate tumour volumes estimated based on MPMRI consistently overestimated histological reference tumour volumes. The variability of tumour volume estimates across the different pulse sequences exceeded interobserver variability within any sequence. Tumour volume estimates on DCE MRI provided the lowest inter-observer variability and the highest correlation with histology tumour volumes, whereas the apparent diffusion coefficient (ADC) maps provided the lowest volume estimation error. If validated on a larger data set, the observed correlations could support the development of automated prostate tumour volume segmentation algorithms as well as correction schemes for tumour burden estimation on MPMRI.

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Automatic model‐based semantic registration of multimodal MRI knee data

Xue

Doellinger

Fripp

et al. 2014

Magnetic Resonance Imaging

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Registration of prostate histology images to ex vivo MR images via strand‐shaped fiducials

Abstract: The proposed method registers digital histology to prostate MR images, yielding 70% reduced processing time and mean accuracy sufficient to achieve 85% overlap on histology and ex vivo MR images for a 0.2 cc spherical tumor.

Cited by 58 publications

References 36 publications

Registration of Whole-Mount Histology and Volumetric Imaging of the Prostate Using Particle Filtering

Registration of Whole-Mount Histology and Volumetric Imaging of the Prostate Using Particle Filtering

Accuracy and variability of tumor burden measurement on multi-parametric MRI

Automatic model‐based semantic registration of multimodal MRI knee data

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