Computer Aided Detection of Prostate Cancer on Biparametric MRI Using a Quadratic Discriminant Model

Jensen, Carina; Korsager, Anne Sofie; Boesen, Lars; Østergaard, Lasse Riis; Carl, Jesper

doi:10.1007/978-3-319-59126-1_14

Cited by 1 publication

(1 citation statement)

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“…Carina et al, extracted image intensity, gradient, gradient direction and distance features from T2weighted (T2W), diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) MRI series. These features are then used as input to the secondary discriminant analysis model to detect prostate cancer [24]. Wang et al, proposed a stack-based ensemble learning method for the detection of prostate cancer, which can simultaneously construct a diagnostic model and extract interpretable diagnostic rules [25].…”

Section: Related Workmentioning

confidence: 99%

ProCDet: A New Method for Prostate Cancer Detection Based on MR Images

Ye¹,

Zhang²,

Wang³

2021

IEEE Access

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Prostate cancer is a malignant tumor that occurs in the male prostate. Prostate cancer lesions have the characteristics of small size and blurry outline, which is a challenge to design a robust prostate cancer detection method. At present, clinical diagnosis of prostate cancer is mainly based on magnetic resonance (MR) imaging. However, it is difficult to obtain prostate cancer data, and the data with true values is also very limited, which further increases the difficulty of prostate cancer detection methods based on MR images. To solve these problems, this paper designs a new method of prostate cancer detection based on MR images, which is recorded as ProCDet. The method consists of three modules: registration of prostate MR images, segmentation of prostate, and segmentation of prostate cancer lesions. First, the registration between different sequences of MR images is performed to find the spatial relationship between the different sequences. Then, the designed prostate segmentation network based on the attention mechanism is used to segment the prostate to remove the interference of background information. Finally, a 3D prostate cancer lesion segmentation network based on Focal Tversky Loss is applied to determine the specific location of prostate cancer. Moreover, in order to take full advantage of unlabeled prostate data, this paper designs a self-supervised learning method to improve the accuracy of prostate cancer detection. The proposed ProCDet has been experimentally verified on the ProstateX dataset. When the average number of false-positive lesions per patient is 0.6275, the true-positive rate is 91.82%. Experimental results show that the ProCDet can obtain competitive detection performance.

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Section: Related Workmentioning

confidence: 99%