Computerized detection and recognition of follicles in ovarian ultrasound images: a review

Performance Analysis of a Novel Hybrid Segmentation Method for Polycystic Ovarian Syndrome Monitoring

Experts have used ultrasound imaging to manually determine follicle count and perform measurements, especially in cases of polycystic ovary syndrome (PCOS). However, due to the laborious and error-prone process of manual diagnosis, researchers have explored and developed medical image processing techniques to help with diagnosing and monitoring PCOS. This study proposes a combination of Otsu's thresholding with the Chan-Vese method to segment and identify follicles in the ovary with reference to ultrasound images marked by a medical practitioner. Otsu's thresholding highlights the pixel intensities of the image and creates a binary mask for use with the Chan-Vese method to define the boundary of the follicles. The acquired results were compared between the classical Chan-Vese method and the proposed method. The performances of the methods were evaluated in terms of accuracy, Dice score, Jaccard index and sensitivity. In overall segmentation evaluation, the proposed method showed superior results compared to the classical Chan-Vese method. Among the calculated evaluation metrics, the sensitivity of the proposed method was superior, with an average of 0.74 ± 0.12. Meanwhile, the average sensitivity for the classical Chan-Vese method was 0.54 ± 0.14, which is 20.03% lower than the sensitivity of the proposed method. Moreover, the proposed method showed significantly improved Dice score (p = 0.011), Jaccard index (p = 0.008) and sensitivity (p = 0.0001). This study showed that the combination of Otsu's thresholding and the Chan-Vese method enhanced the segmentation of ultrasound images.

An improvised follicle polycystic ovarian detection using AKF from a sequence of given ultrasound images

The segmentation of images is a technique used to extract information from a digital picture. One of the main applications in image segmentation is especially in medical image processing detection of an abnormal aspect to diagnose diseases. Ovarian cysts are formed in women who are unbalanced in estrogen and progesterone hormones. The polycystic ovarian syndrome is known as this condition. Women have a fluid collection in their ovaries called follicles. The image captures the follicles by ultrasound scanning. The detection of follicles from the echo sound image requires an optimized segmentation algorithm. Quantification of the ovary and follicle volumes and follicle counts for diagnosis and management in assisted replication is performed in clinical practice. Now for a few days, most women face infertility problems in the age group between 22 and 35. To analyze and classify the problems, the decision can start with the use of advanced technology to structurally compare the normal ovary to the affected ovary. Ovarian imagery is an effective instrument for the treatment of infertility. In human reproduction, follicle monitoring is particularly important. The primary method of doctors’ assessment is a periodic measurement of the size and form of follicles over several days. The field of medical imaging is one of the most popular applications of image processing techniques. The widespread popularity of image analysis technology in the field of diagnostic devices is due to the advancement of advanced imaging instruments combined with developments in algorithms unique to medical image processing, both for diagnostic tests and therapeutic preparation. Ultrasound imaging is a technique that uses high-frequency sound waves to capture images from within the human body. The echoes of reflected sound waves are captured and shown in real-time. It’s a good way to look at the nucleus, liver, kidneys, gall bladder, and ovaries, among other internal organs. The main contribution lies in identifying dominant follicles, that is growing and capable of producing an egg after the follicular phase, which is our primary goal, and this is where our suggested study comes in. Follicular ovulation doesn’t occur in all women, and not all of the dominant follicular levels are strong enough just to result in a pregnancy. Today, the follicles monitor human interaction using non-automatic means. Our proposed approach for the detection of follicle polycystic ovarian using AKF is not only helpful for generating highly efficient results but also proves to be best when compared with the state of art results from the existing methods.

Deeply-Supervised 3D Convolutional Neural Networks for Automated Ovary and Follicle Detection from Ultrasound Volumes

Automated detection of ovarian follicles in ultrasound images is much appreciated when its effectiveness is comparable with the experts’ annotations. Today’s best methods estimate follicles notably worse than the experts. This paper describes the development of two-stage deeply-supervised 3D Convolutional Neural Networks (CNN) based on the established U-Net. Either the entire U-Net or specific parts of the U-Net decoder were replicated in order to integrate the prior knowledge into the detection. Methods were trained end-to-end by follicle detection, while transfer learning was employed for ovary detection. The USOVA3D database of annotated ultrasound volumes, with its verification protocol, was used to verify the effectiveness. In follicle detection, the proposed methods estimate follicles up to 2.9% more accurately than the compared methods. With our two-stage CNNs trained by transfer learning, the effectiveness of ovary detection surpasses the up-to-date automated detection methods by about 7.6%. The obtained results demonstrated that our methods estimate follicles only slightly worse than the experts, while the ovaries are detected almost as accurately as by the experts. Statistical analysis of 50 repetitions of CNN model training proved that the training is stable, and that the effectiveness improvements are not only due to random initialisation. Our deeply-supervised 3D CNNs can be adapted easily to other problem domains.

Public database for validation of follicle detection algorithms on 3D ultrasound images of ovaries

BACKGROUND AND OBJECTIVE

Automated follicle detection in ovarian ultrasound volumes remains a challenging task. An objective comparison of different follicle-detection approaches is only possible when all are tested on the same data. This paper describes the development and structure of the first publicly accessible USOVA3D database of annotated ultrasound volumes with ovarian follicles.

METHODS

The ovary and all follicles were annotated in each volume by two medical experts. The USOVA3D database is supplemented by a general verification protocol for unbiased assessment of detection algorithms that can be compared and ranked by scoring according to this protocol. This paper also introduces two baseline automated follicle-detection algorithms, the first based on Directional 3D Wavelet Transform (3D DWT) and the second based on Convolutional Neural Networks (CNN).

RESULTS

The USOVA3D testing data set was used to verify the variability and reliability of follicle annotations. The intra-rater overall score yielded around 83 (out of a maximum of 100), while both baseline algorithms pointed out just a slightly lower performance, with the 3D DWT-based algorithm being better, with an overall score around 78.

CONCLUSIONS

On the other hand, the development of the CNN-based algorithm demonstrated that the USOVA3D database contains sufficient data for successful training without overfitting. The inter-rater reliability analysis and the obtained statistical metrics of effectiveness for both baseline algorithms confirmed that the USOVA3D database is a reliable source for developing new automated detection methods.

Directional 3D Wavelet Transform Based on Gaussian Mixtures for the Analysis of 3D Ultrasound Ovarian Volumes

The success of in-vitro fertilization can be predicted by a correct quantitative and qualitative assessment of ovarian follicles. Several ovarian follicle detection and recognition algorithms have been published. Their effectiveness is inferior to human follicle annotations due to various kinds of noise, degradations, and artefacts in ultrasonic images. This paper deals with an approach to recognize antral follicles from 2 mm in diameter in 3D ultrasound data. Its detection phase looks for candidate follicular regions, while the recognition phase assesses the likelihood of a region to correspond to a follicle. Three innovative definitions underpin the detection: Laplacian-of-Gaussian-based directional 3D wavelet transform, adaptive multiscale search based on Gaussian mixtures, and recursive convexity-based region splitting. A likelihood index is also introduced to support follicle recognition. The proposed approach was tested on 30 ultrasound ovarian volumes generated by different sonographic machines in stimulated and non-stimulated examination cycles. The obtained follicle recognition rates exceed those of the best 3D approaches known by about 10 percent, while qualitative assessments yield comparable values.

Automated ovarian follicular monitoring: A novel real-time approach

Ovarian follicular monitoring is an essential diagnostic tool in obstetrics and gynecology to evaluate ovarian reserve and to estimate follicular and ovarian response to fertility treatment. Given the significant time requirement, inconvenience measuring follicles and estimating follicular development during multiple examinations, and variable results of different clinicians performing monitoring, complete automation of follicular monitoring is necessary. Computerized follicle detection is currently either semi-automated or has low performance due to limiting factors: (1) noise, (2) detecting multiple follicles very close to each other as one follicle region without finding the boundary of individual follicles, and (3) not being fast enough to be used in real-time clinical practice. To overcome these limitations, we handle noise by singular value decomposition based image compression followed by an anisotropic diffusion scheme for multiplicative speckle, and detect follicles by performing different segmentation techniques depending on features of the image (such as pixel intensity level) and features of the detected follicle areas (such as roundness). This approach allows for rapid identification and measurement of individual follicles with the ability to differentiate between the borders of adjacent follicles and the boundary between the follicle and ovarian stroma.

Estimating the chance of success in IVF treatment using a ranking algorithm

In medicine, estimating the chance of success for treatment is important in deciding whether to begin the treatment or not. This paper focuses on the domain of in vitro fertilization (IVF), where estimating the outcome of a treatment is very crucial in the decision to proceed with treatment for both the clinicians and the infertile couples. IVF treatment is a stressful and costly process. It is very stressful for couples who want to have a baby. If an initial evaluation indicates a low pregnancy rate, decision of the couple may change not to start the IVF treatment. The aim of this study is twofold, firstly, to develop a technique that can be used to estimate the chance of success for a couple who wants to have a baby and secondly, to determine the attributes and their particular values affecting the outcome in IVF treatment. We propose a new technique, called success estimation using a ranking algorithm (SERA), for estimating the success of a treatment using a ranking-based algorithm. The particular ranking algorithm used here is RIMARC. The performance of the new algorithm is compared with two well-known algorithms that assign class probabilities to query instances. The algorithms used in the comparison are Naïve Bayes Classifier and Random Forest. The comparison is done in terms of area under the ROC curve, accuracy and execution time, using tenfold stratified cross-validation. The results indicate that the proposed SERA algorithm has a potential to be used successfully to estimate the probability of success in medical treatment.