| 1. |
- Castella, Cyril, et al.
(författare)
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Masses detection in breast tomosynthesis and digital mammography: a model observer study
- 2009
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Ingår i: [Host publication title missing]. - : SPIE. ; 7263
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Konferensbidrag (refereegranskat)abstract
- In this study, we adapt and apply model observers within the framework of realistic detection tasks in breast tomosynthesis (BT). We use images consisting of realistic masses digitally embedded in real patient anatomical backgrounds, and we adapt specific model observers that have been previously applied to digital mammography (DM). We design alternative forced-choice experiments (AFC) studies for DM and BT tasks in the signal known exactly but variable (SKEV) framework. We compare performance of various linear model observers (non-prewhitening matched filter with an eye filter, and several channelized Hotelling observers (CHO) against human. A good agreement in performance between human and model observers can be obtained when an appropriate internal noise level is adopted. Models achieve the same detection performance across BT and DM with about three times less projected signal intensity in BT than in DM (humans: 3.8), due to the anatomical noise reduction in BT. We suggest that, in the future, model observers can potentially be used as an objective tool for automating the optimization of BT acquisition parameters or reconstruction algorithms, or narrowing a wide span of possible parameter combinations, without requiring human observers studies.
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| 2. |
- Diaz, Ivan, et al.
(författare)
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Derivation of an Observer Model Adapted to Irregular Signals Based on Convolution Channels
- 2015
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Ingår i: IEEE Transactions on Medical Imaging. - 1558-254X. ; 34:7, s. 1428-1435
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Tidskriftsartikel (refereegranskat)abstract
- Anthropomorphic model observers are mathe-matical algorithms which are applied to images with the ultimate goal of predicting human signal detection and classification accuracy across varieties of backgrounds, image acquisitions and display conditions. A limitation of current channelized model observers is their inability to handle irregularly-shaped signals, which are common in clinical images, without a high number of directional channels. Here, we derive a new linear model observer based on convolution channels which we refer to as the "Filtered Channel observer" (FCO), as an extension of the channelized Hotelling observer (CHO) and the nonprewhitening with an eye filter (NPWE) observer. In analogy to the CHO, this linear model observer can take the form of a single template with an external noise term. To compare with human observers, we tested signals with irregular and asymmetrical shapes spanning the size of lesions down to those of microcalfications in 4-AFC breast tomosynthesis detection tasks, with three different contrasts for each case. Whereas humans uniformly outperformed conventional CHOs, the FCO observer outperformed humans for every signal with only one exception. Additive internal noise in the models allowed us to degrade model performance and match human performance. We could not match all the human performances with a model with a single internal noise component for all signal shape, size and contrast conditions. This suggests that either the internal noise might vary across signals or that the model cannot entirely capture the human detection strategy. However, the FCO model offers an efficient way to apprehend human observer performance for a non-symmetric signal.
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