Abstract
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.
Highlights
The prostate belongs to the male reproductive system and is a gland unique to men
To realize the automatic diagnosis of prostate cancer, this paper proposes a new method for detecting prostate cancer based on magnetic resonance (MR) images, which is recorded as prostate cancer detection (ProCDet)
ProstateX is a medical image recognition competition jointly initiated by the American Association of Physicists in Medicine (AAPM), the International Society for Optics and Photonics (SPIE) and the National Cancer Institute (NCI)
Summary
The prostate belongs to the male reproductive system and is a gland unique to men. The prostate is mainly composed of three regions: the transition zone, the central zone and the peripheral zone [1]. MRI is a commonly used imaging method in the diagnosis of prostate cancer Through this method, doctors can clearly observe the prostate structure and pathological information, thereby greatly improving the sensitivity and specificity of diagnosis [8]. To realize the automatic diagnosis of prostate cancer, this paper proposes a new method for detecting prostate cancer based on MR images, which is recorded as ProCDet. ProCDet can automatically segment the prostate from the MR image, and further obtain information such as the location, boundary and volume of the prostate cancer lesion. ProCDet can automatically segment the prostate from the MR image, and further obtain information such as the location, boundary and volume of the prostate cancer lesion This improves the doctor's reading efficiency, and greatly relieves the doctor's work pressure. (3) The self-supervised learning network model is adopted as a pre-training model, and the 3D prostate cancer lesion segmentation network based on Focal Tversky Loss is used to determine the specific location of the prostate cancer
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