Correlating Tumour Histology and ex vivo MRI Using Dense Modality-Independent Patch-Based Descriptors

Hallack, Andre; Papież, Bartłomiej W.; Wilson, James M.; Wang, Lai Mun; Maughan, Tim; Gooding, Mark J.; Schnabel, Julia A.

doi:10.1007/978-3-319-28194-0_17

Cited by 6 publications

(12 citation statements)

References 13 publications

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“…However, in the majority of cases, the result of initialization is of sufficient quality and the algorithm does not need any intervention. Additional improvements to this step could be achieved with one of the multimodal histology 2D-2D registration algorithms (Jacobs et al, 1999;du Bois d'Aische et al, 2005;Li et al, 2006;Pitiot et al, 2006;Hallack et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

“…Hallack et al . () performed a three‐stage procedure for the registration of a histology stack to an ex‐vivo MRI dataset using feature points: (1) Matching image stack to MRI dataset, (2) rigid registration of each histological slide to MRI slice (3) and nonrigid registration. Some of the methods rely on implanting artificial markers (Humm et al ., ; Lazebnik et al ., ; Breen et al ., ) or color‐coding (Alic et al ., ).…”

Section: Introductionmentioning

confidence: 99%

“…One way of determining this information is to use a complementary modality such as blockface photographs (Kim et al, 1997;Schormann & Zilles, 1998;Meyer et al, 2006;Dauguet et al, 2007;Park et al, 2008;Liu et al, 2012;Goubran et al, 2013;Hallack et al, 2015) or photographs of an unstained brain (Bardinet et al, 2002). Hallack et al (2015) performed a three-stage procedure for the registration of a histology stack to an ex-vivo MRI dataset using feature points: (1) Matching image stack to MRI dataset, (2) rigid registration of each histological slide to MRI slice (3) and nonrigid registration. Some of the methods rely on implanting artificial markers (Humm et al, 2003;Lazebnik et al, 2003;Breen et al, 2005) or color-coding (Alic et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

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Automatic deformable registration of histological slides to μCT volume data

Chicherova

Hieber

Khimchenko

et al. 2018

Journal of Microscopy

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Localizing a histological section in the three-dimensional dataset of a different imaging modality is a challenging 2D-3D registration problem. In the literature, several approaches have been proposed to solve this problem; however, they cannot be considered as fully automatic. Recently, we developed an automatic algorithm that could successfully find the position of a histological section in a micro computed tomography (μCT) volume. For the majority of the datasets, the result of localization corresponded to the manual results. However, for some datasets, the matching μCT slice was off the ground-truth position. Furthermore, elastic distortions, due to histological preparation, could not be accounted for in this framework. In the current study, we introduce two optimization frameworks based on normalized mutual information, which enabled us to accurately register histology slides to volume data. The rigid approach allocated 81 % of histological sections with a median position error of 8.4 μm in jaw bone datasets, and the deformable approach improved registration by 33 μm with respect to the median distance error for four histological slides in the cerebellum dataset.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic deformable registration of histological slides to μCT volume data

Chicherova

Hieber

Khimchenko

et al. 2018

Journal of Microscopy

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“…Hallack et al. used the diffeomorphic logDemons image registration method, but with a dense scale invariant feature transform (SIFT) as similarity measure . Each landmark P j was tracked independently and sequentially using image registration around a region of interest ( W j ) .…”

Section: Methodsmentioning

confidence: 99%

“…Then, its center and radii are re-estimated as in 56 58 but with a dense scale invariant feature transform (SIFT) 59 as similarity measure. 60,61 Each landmark P j was tracked independently and sequentially using image registration around a region of interest (W j ). 62 For frame I(t), W j,t À 1 (t) of size 51 9 51 pixels is extracted around the previous estimated landmark location P j (t À 1).…”

Section: B2 2d Trackingmentioning

confidence: 99%

Evaluation of 2D and 3D ultrasound tracking algorithms and impact on ultrasound‐guided liver radiotherapy margins

et al. 2018

Self Cite

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Liver motion estimation and prediction during free-breathing from 2D ultrasound images can substantially reduce the in-plane motion uncertainty and hence treatment margins. Employing an accurate tracking method while avoiding non-linear temporal prediction would be favorable. This approach has the potential to shorten treatment time compared to breath-hold and gated approaches, and increase treatment efficiency and safety.

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