Abstract
PurposeWe propose a deep learning-based image interpretation system for skeleton segmentation and extraction of hot spots of bone metastatic lesion from a whole-body bone scintigram followed by automated measurement of a bone scan index (BSI), which will be clinically useful.MethodsThe proposed system employs butterfly-type networks (BtrflyNets) for skeleton segmentation and extraction of hot spots of bone metastatic lesions, in which a pair of anterior and posterior images are processed simultaneously. BSI is then measured using the segmented bones and extracted hot spots. To further improve the networks, deep supervision (DSV) and residual learning technologies were introduced.ResultsWe evaluated the performance of the proposed system using 246 bone scintigrams of prostate cancer in terms of accuracy of skeleton segmentation, hot spot extraction, and BSI measurement, as well as computational cost. In a threefold cross-validation experiment, the best performance was achieved by BtrflyNet with DSV for skeleton segmentation and BtrflyNet with residual blocks. The cross-correlation between the measured and true BSI was 0.9337, and the computational time for a case was 112.0 s.ConclusionWe proposed a deep learning-based BSI measurement system for a whole-body bone scintigram and proved its effectiveness by threefold cross-validation study using 246 whole-body bone scintigrams. The automatically measured BSI and computational time for a case are deemed clinically acceptable and reliable.
Highlights
Radionuclide imaging is a useful means of examining patients who may have metastasis of the prostate, breast or lung cancers, which are common cancers globally [1, 2]
Soloway et al [5] proposed the extent of disease (EOD), which categorises bone scan examinations into five grades based on the number of bone metastases
This study presents a system consisting of skeleton segmentation and extraction of hot spots of bone metastatic lesion followed by bone scan index (BSI) measurement
Summary
Radionuclide imaging is a useful means of examining patients who may have metastasis of the prostate, breast or lung cancers, which are common cancers globally [1, 2]. A typical screening method is bone scintigraphy, which uses Tc-99 m-methylene diphosphonate (MDP) [3] or Tc-99 m-hydroxymethylene diphosphonate (HMDP) [4] agents. Because visual interpretation of the bone scintigram lacks quantitative and reproducible diagnosis, quantitative indices have been proposed. Soloway et al [5] proposed the extent of disease (EOD), which categorises bone scan examinations into five grades based on the number of bone metastases. It is simple but not suitable for detailed diagnosis. Erdi et al [6] proposed the bone scan index (BSI), which standardises the assessment of bone scans [7], and they presented a region growing-based semiautomated bone metastatic lesion extraction method to measure the BSI.
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