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Multi-stage fully convolutional network for precise prostate segmentation in ultrasound images

Feng Yujie, Atabansi Chukwuemeka Clinton, Nie Jing, Liu Haijun, Zhou Hang, Zhao Huai, Hong Ruixia, Li Fang, Zhou Xichuan

Biocybernetics and Biomedical Engineering

2023

Vol. 43, no. 3

586--602

Prostate cancer is one of the most commonly diagnosed non-cutaneous malignant tumors and the sixth major cause of cancer-related death generally found in men globally. Automatic segmentation of prostate regions has a wide range of applications in prostate cancer diagnosis and treatment. It is challenging to extract powerful spatial features for precise prostate segmentation methods due to the wide variation in prostate size, shape, and histopathologic heterogeneity among patients. Most of the existing CNN-based architectures often produce unsatisfactory results and inaccurate boundaries in prostate segmentation, which are caused by inadequate discriminative feature maps and the limited amount of spatial information. To address these issues, we propose a novel deep learning technique called Multi-Stage FCN architecture for 2D prostate segmentation that captures more precise spatial information and accurate prostate boundaries. In addition, a new prostate ultrasound image dataset known as CCH-TRUSPS was collected from Chongqing University Cancer Hospital, including prostate ultrasound images of various prostate cancer architectures. We evaluate our method on the CCH-TRUSPS dataset and the publicly available Multi-site T2-weighted MRI dataset using five commonly used metrics for medical image analysis. When compared to other CNN-based methods on the CCH-TRUSPS test set, our Multi-Stage FCN achieves the highest and best binary accuracy of 99.15%, the DSC score of 94.90%, the IoU score of 89.80%, the precision of 94.67%, and the recall of 96.49%. The statistical and visual results demonstrate that our approach outperforms previous CNN-based techniques in all ramifications and can be used for the clinical diagnosis of prostate cancer.

Analiza kształtu struktur anatomicznych jamy brzusznej dla potrzeb radioterapii nowotworu prostaty

Skalski A., Łągwa J., Kędzierawski P., Kukołowicz P.

Pomiary Automatyka Kontrola

2013

R. 59, nr 3

254--257

W artykule przedstawiono algorytmy opisu kształtu, które mogą zostać wykorzystane do budowy wiedzy a priori, o którą można wzbogacić metody segmentacji danych medycznych. Opisana metodologia została wykorzystana do analizy kształtu struktur anatomicznych okolicy miednicy. Przeprowadzona analiza pozwoliła sprawdzić zmienność geometrii struktur anatomicznych istotnych z punktu widzenia radioterapii nowotworu prostaty, Zmienność kształtu organów oceniono zarówno: pomiędzy osobami w populacji chorych z nowotworem gruczołu krokowego jak i zmienność tych kształtów podczas procesu radioterapeutycznego u pacjenta.

Prostate cancer is one of most frequently diagnosed cancer diseases among men population, especially in Europe and the USA. The number of fatal cases is also significant. It leads to many attempts to improve processes of the cancer diagnosis and therapy. One of most promising methods of treatment is radiation therapy. However, its proper planning requires contouring of every important structure on every slice obtained from the imaging equipment (in example a CT scanner), which is time-consuming for medical staff. To solve this problem, many efforts are made to construct algorithms of automatic segmentation of organs in 3D data. To provide the expected efficiency of such methods, a base of a priori knowledge about organs to be delineated is desired. In this paper we present shape description algorithms which could be used to collect the a priori knowledge, potentially able to improve the medical data segmentation methods. The described methodology was used in shape analysis of pelvic region structures, important for planning the prostate cancer radiation therapy, which included: GTV (Gross Tumor Volume), rectum, bladder and femoral heads. In this paper 5 different algorithms are presented. The first proposed method describes the shape of the analyzed organ with parameters (semi-axis lengths) of minimum-volume ellipsoid circumscribed on the structure. The other algorithms provide the information about the shape of the analyzedstructure as a distribution of chosen geometric quantity values (such as distance) between the groups of points randomly selected on its surface. The proposed algorithms were tested on the organ models reconstructed from the structures contoured on the images obtained from CT. As a result of the performed analysis, geometrical variability of the considered structures were specified. Variability of shapes of the analyzed organs was examined for the patients from the population group of men with diagnosed prostate cancer as well as for the single patient cases during radiation therapy.

Texture analysis in perfusion images of prostate cancer-A case study

Śmietański J., Tadeusiewicz R., Łuczyńska E.

International Journal of Applied Mathematics and Computer Science

2010

Vol. 20, no 1

149-156

The analysis of prostate images is one of the most complex tasks in medical images interpretation. It is sometimes very difficult to detect early prostate cancer using currently available diagnostic methods. But the examination based on perfusion computed tomography (p-CT) may avoid such problems even in particularly difficult cases. However, the lack of computational methods useful in the interpretation of perfusion prostate images makes it unreliable because the diagnosis depends mainly on the doctor's individual opinion and experience. In this paper some methods of automatic analysis of prostate perfusion tomographic images are presented and discussed. Some of the presented methods are adopted from papers of other researchers, and some are elaborated by the authors. This presentation of the method and algorithms is important, but it is not the master scope of the paper. The main purpose of this study is computational (deterministic and independent) verification of the usefulness of the p-CT technique in a specific case. It shows that it is possible to find computationally attainable properties of p-CT images which allow pointing out the cancerous lesion and can be used in computer aided medical diagnosis.