Framework for the co-registration of MRI and Histology Images in Prostate Cancer Patients with Radical Prostatectomy

被引:4
|
作者
Rusu, Mirabela [1 ]
Kunder, Christian [2 ]
Fan, Richard [3 ]
Ghanouni, Pejman [1 ]
West, Robert [2 ]
Sonn, Geoffrey [3 ]
Brooks, James [3 ]
机构
[1] Stanford Univ, Dept Radiol, 300 Pasteur Dr, Stanford, CA 94305 USA
[2] Stanford Univ, Dept Pathol, 300 Pasteur Dr, Stanford, CA 94305 USA
[3] Stanford Univ, Dept Urol, 300 Pasteur Dr, Stanford, CA 94305 USA
来源
关键词
radiology-pathology correlation; MRI signatures; prostate cancer; radical prostatectorny; indolent cancer; aggressive cancer; clinically significant cancers;
D O I
10.1117/12.2513099
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Prostate magnetic resonance imaging (MRI) allows the detection and treatment planning of clinically significant cancers. However, indolent cancers, e.g., those with Gleason scores 3+3, are not readily distinguishable on MRI. Titus an image-guided biopsy is still required before proceeding with a radical treatment for aggressive tumors or considering active surveillance for indolent disease. The excision of the prostate as part of radical prostatectomy treatments provides a unique opportunity to correlate whole-mount histology slices with Mill. Through a careful spatial alignment of histology slices and NI the extent of aggressive and indolent disease can be mapped on MRI which allows one to investigate Mill-derived features that might be able to distinguish aggressive from indolent cancers. Here, we introduce a framework for the 31) spatial integration of radiology and pathology images in the prostate. Our approach, first, uses groupwise-registration methods to reconstruct the histology specimen prior to sectioning, and incorporates the MRI as a spatial constraint, and, then, performs a multi-modal 3D affine and deformable alignment between the reconstructed histology specimen and the MRI. tested our approach on 15 studies and found a Dice similarity coefficient of 0.94+0.02 and a urethra deviation of 1.11+0.34 mm between the histology reconstruction and the MRI. Our robust framework successfully mapped the extent of disease from histology slices on MRI and created ground truth labels for characterizing aggressive and indolent disease on MRI.
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页数:4
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