Milan Pantelic

Zhenzhen Dai, Ivan Jambor, Pekka Taimen et al.

Purpose: We aimed to develop deep machine learning (DL) models to improve the detection and segmentation of intraprostatic lesions (IL) on bp-MRI by using whole amount prostatectomy specimen-based delineations. We also aimed to investigate whether transfer learning and self-training would improve results with small amount labelled data. Methods: 158 patients had suspicious lesions delineated on MRI based on bp-MRI, 64 patients had ILs delineated on MRI based on whole mount prostatectomy specimen sections, 40 patients were unlabelled. A non-local Mask R-CNN was proposed to improve the segmentation accuracy. Transfer learning was investigated by fine-tuning a model trained using MRI-based delineations with prostatectomy-based delineations. Two label selection strategies were investigated in self-training. The performance of models was evaluated by 3D detection rate, dice similarity coefficient (DSC), 95 percentile Hausdrauff (95 HD, mm) and true positive ratio (TPR). Results: With prostatectomy-based delineations, the non-local Mask R-CNN with fine-tuning and self-training significantly improved all evaluation metrics. For the model with the highest detection rate and DSC, 80.5% (33/41) of lesions in all Gleason Grade Groups (GGG) were detected with DSC of 0.548[0.165], 95 HD of 5.72[3.17] and TPR of 0.613[0.193]. Among them, 94.7% (18/19) of lesions with GGG > 2 were detected with DSC of 0.604[0.135], 95 HD of 6.26[3.44] and TPR of 0.580[0.190]. Conclusion: DL models can achieve high prostate cancer detection and segmentation accuracy on bp-MRI based on annotations from histologic images. To further improve the performance, more data with annotations of both MRI and whole amount prostatectomy specimens are required.

Zhenzhen Dai, Eric Carver, Chang Liu et al.

Prostate cancer (PCa) is the most common cancer in men in the United States. Multiparametic magnetic resonance imaging (mp-MRI) has been explored by many researchers to targeted prostate biopsies and radiation therapy. However, assessment on mp-MRI can be subjective, development of computer-aided diagnosis systems to automatically delineate the prostate gland and the intraprostratic lesions (ILs) becomes important to facilitate with radiologists in clinical practice. In this paper, we first study the implementation of the Mask-RCNN model to segment the prostate and ILs. We trained and evaluated models on 120 patients from two different cohorts of patients. We also used 2D U-Net and 3D U-Net as benchmarks to segment the prostate and compared the model's performance. The contour variability of ILs using the algorithm was also benchmarked against the interobserver variability between two different radiation oncologists on 19 patients. Our results indicate that the Mask-RCNN model is able to reach state-of-art performance in the prostate segmentation and outperforms several competitive baselines in ILs segmentation.

Weiwei Zong, Joon Lee, Chang Liu et al.

Deep learning models have had a great success in disease classifications using large data pools of skin cancer images or lung X-rays. However, data scarcity has been the roadblock of applying deep learning models directly on prostate multiparametric MRI (mpMRI). Although model interpretation has been heavily studied for natural images for the past few years, there has been a lack of interpretation of deep learning models trained on medical images. This work designs a customized workflow for the small and imbalanced data set of prostate mpMRI where features were extracted from a deep learning model and then analyzed by a traditional machine learning classifier. In addition, this work contributes to revealing how deep learning models interpret mpMRI for prostate cancer patients stratification.