Mr. Hanjoo Jang Profile

Hanjoo Jang

at Yonsei Univ

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Proceedings Article | 29 March 2024 Presentation + Paper

Understanding the generalizability of a convolutional neural network-based model observer for breast tomosynthesis images with different volume glandular fractions

Hanjoo Jang, Jongduk Baek

Proceedings Volume 12929, 129290S (2024) https://doi.org/10.1117/12.3004908

KEYWORDS: Breast, Signal detection, Tomosynthesis, Convolutional neural networks

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In this study, we aimed to understand the generalizability of a convolutional neural network (CNN)-based model observer for breast tomosynthesis images with two different (i.e., 30% and 50%) volume glandular fractions (VGFs). Spiculated signal with a volume equivalent to that of a spherical signal with a diameter of 1 mm was inserted at the center to generate signal-present breast volumes. The networks were optimized through brute force search in terms of depth (i.e., 5, 10, and 15 convolutional blocks) to investigate whether there is any correlation between the detection performance, and the difference between the theoretical receptive field (TRF) size of the network and the signal size. For all cases, the optimal detection performance of the CNN-based model observer was achieved when 5 convolutional blocks (i.e., TRF size of 1.1 mm) were used. To verify whether a nonlinear framework improves the generalizability of the observer, the detection performance of the CNN-based model observer was compared to that of the Hoteling observer (HO). A total of 18 tests were conducted by applying the optimal networks (i.e., N_30%, N_50%, N_both) and the Hotelling templates (i.e., HT_30%, HT_50%, and HT_both) to each of the three testing subsets in order to compare the generalizability between the two observers. The CNN-based model observer showed a better generalized detection performance compared to that of the HO.

Proceedings Article | 7 March 2018 Paper

Lesion detection performance of cone beam CT images with anatomical background noise: single-slice vs. multi-slice human and model observer study

Minah Han, Hanjoo Jang, Jongduk Baek

Proceedings Volume 10577, 105770N (2018) https://doi.org/10.1117/12.2286028

KEYWORDS: Computed tomography, Signal detection, Performance modeling

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We investigate lesion detectability and its trends for different noise structures in single-slice and multislice CBCT images with anatomical background noise. Anatomical background noise is modeled using a power law spectrum of breast anatomy. Spherical signal with a 2 mm diameter is used for modeling a lesion. CT projection data are acquired by the forward projection and reconstructed by the Feldkamp-Davis-Kress algorithm. To generate different noise structures, two types of reconstruction filters (Hanning and Ram-Lak weighted ramp filters) are used in the reconstruction, and the transverse and longitudinal planes of reconstructed volume are used for detectability evaluation. To evaluate single-slice images, the central slice, which contains the maximum signal energy, is used. To evaluate multislice images, central nine slices are used. Detectability is evaluated using human and model observer studies. For model observer, channelized Hotelling observer (CHO) with dense difference-of-Gaussian (D-DOG) channels are used. For all noise structures, detectability by a human observer is higher for multislice images than single-slice images, and the degree of detectability increase in multislice images depends on the noise structure. Variation in detectability for different noise structures is reduced in multislice images, but detectability trends are not much different between single-slice and multislice images. The CHO with D-DOG channels predicts detectability by a human observer well for both single-slice and multislice images.

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