Denetimsiz derin öğrenme kullanılarak dijital meme tomosentezi görüntülerinde gürültünün giderilmesi
Unsupervised denoising of digital breast tomosynthesis images using deep learning
Yazar: OSMAN ALPCAN ÖZCAN
Danışman: DOÇ. DR. İSA YILDIRIM
İstanbul Teknik Üniversitesi / Lisansüstü Eğitim Enstitüsü / Elektronik ve Haberleşme Mühendisliği Ana Bilim Dalı / Biyomedikal Mühendisliği Bilim Dalı
Derece: Yüksek Lisans, 2023
Konu: Elektrik ve Elektronik Mühendisliği = Electrical and Electronics Engineering

Özet:
Meme kanseri, kadınlarda en yaygın kanser türüdür ve genellikle süt bezi dokusu hücrelerinin kontrolsüz ve aşırı çoğalması sonucu ortaya çıkar. Erken aşamada teşhis edilmezse ölümcül hale gelebilir. Erken aşama teşhisinde en çok tercih edilen görüntüleme yöntemi olan mamografi, 2 boyutlu (2D) bir görüntüleme yöntemidir. 2D yöntemler doğaları gereği bir projeksiyondan görüntü oluştururlar ve bu, dokuların üst üste binmesi nedeniyle yanlış pozitif ve yanlış negatif oranlarının artmasına yol açar; bu oranın artması da ölüm oranlarını artırır. 
Bu durumu önlemek için 3D görüntüleme yöntemleri önerilmiş ve kullanımları artmıştır. Dijital meme tomosentezi (DBT), 3 boyutlu (3D) bir görüntüleme yöntemi olarak tercih edilir. DBT, sınırlı açılarda alınan projeksiyonlarla birlikte 3D bir görüntü oluşturur ve düşük radyasyon dozlarıyla çalışır. 2D görüntülemede oluşan dokuların üst üste binmesi sorunu aşılmış ve böylece dokular daha ayırt edilebilir hale gelmiş ve radyologların erken teşhis oranları artmıştır. Ancak, DBT görüntülerinde sınırlı açı ve düşük radyasyon dozu nedeniyle her projeksiyonun gürültü içereceği ve gürültünün rekonstrüksiyon görüntüsünde artmış şekilde görüneceği kaçınılmazdır. Gürültü giderimi için birçok matematiksel yöntem önerilmiş ve belirli bir ölçüde başarılı oldukları gözlemlenmiştir, ancak hala tatmin edici bir performans sağlama konusunda eksiklikleri vardır. 
Yapay zekâ ve derin öğrenme ağlarının gelişimi ve yaygınlaşmasıyla birlikte, tıbbi görüntü işleme alanında kullanımı da artmıştır. Bu bağlamda, derin öğrenme ağları, tıbbi görüntülerde bir gürültü giderici olarak mekânsal düzenleyicilere alternatif olarak önerilmiştir. Bu çalışmada, bir denetimsiz gürültü giderici sinir ağı, DBT görüntüleri üzerinden gürültüleri ortadan kaldırmak için geliştirilmiştir. Önerilen yöntemin performansını analiz etmek için iki farklı veri seti kullanılmıştır. İlk veri seti kanser görüntü arşivinden BSC-DBT (Meme Kanseri Taraması- Dijital Meme Tomosentezi) veri setidir. Bu veri seti, normal, aksiyon alınması gereken, biyopsi ile kanıtlanmış iyi huylu ve biyopsi ile kanıtlanmış kanser olarak etiketlenmiş 5060 hasta içermektedir. Görüntüler dilimler halinde 2 boyutlu olarak kullanılmıştır. Her kategoriden eşit ve toplam verilerin %70 eğitim verisi, %15 validasyon verisi ve %15 test verisi olacak şekilde belirlenmiştir. Bu veri setine ek olarak DBT-2D Phantom veri seti kullanılmıştır. Toplamda 2148 veri ve yine görüntüler iki boyutlu dilim görüntüsü olacak şekilde veri setinin %70'i eğitim verisi %15'i validasyon verisi ve %15 test verisi olarak organize edilmiştir. Düşük doz ve dar açı kaynaklı en belirgin gürültü şekilleri Gaussian ve Poisson gürültüleri olması sebebiyle gürültü giderici hedefi bu iki gürültü olarak belirlenmiş ve eğitim parametreleri bu gürültülerin giderilmesi üzerine belirlenerek eğitim tamamlanmıştır. 
Önerilen modelin başarısını değerlendirmek için CNR (kontrast-gürültü oranı), PSNR (zirve sinyal gürültü oranı) ve SSIM (yapısal benzerlik indeksi) metrikleri kullanılmıştır. Çalışma sırasında matematiksel yöntemler ile önerilen modelin başarısı metrikler üzerinde kıyaslanmış ve çalışmanın sonucunda önerilen denetimsiz gürültü giderici ağ ile belirlenen metriklerdeki iyileşmenin daha üstün olduğu gözlemlenmiştir. Görüntülerdeki niteliksel iyileşme, önerilen modelinin oldukça umut verici olduğunu göstermektedir.

Abstract:
Breast Cancer is the most common type of cancer in women, usually caused by the uncontrolled and excessive proliferation of glandular tissue cells. It becomes fatal if not diagnosed at an early stage. Before the age of 40, doctors recommend regular manual examinations. After the 40s, imaging methods are added at regular intervals for these examinations. Mammography, a 2-dimensional (2D) imaging method, is the most preferred imaging modality in early-stage diagnosis. By their nature, 2D methods create an image on a projection, and this increases the rate of false positive and false negative rates due to overlapping of tissues. This increased rate often means delayed or incorrect treatment and consequently increases mortality rates.
Ultrasonography has been proposed to prevent this situation. Ultrasonography is used as an additional imaging method to distinguish dense and overlapping tissues on mammography. This means an increase in cost and workload. For this reason, 3D imaging modalities have been proposed and their use has increased in order to perform imaging more effectively, in a shorter time and at a lower cost. Digital breast tomosynthesis (DBT) is preferred as a 3-dimensional (3D) imaging method DBT creates a 3D image with a limited number of projections at wide angles and low radiation doses. The problem of overlapping of the tissues formed in 2D imaging has been overcome, thus the dense tissues have become more distinguishable and the early detection rates of radiologists have been increased. However, in DBT images, due to the limited number of projections and low radiation dose, it is inevitable that each projection will contain noise and that the noise will appear amplified in the reconstructed image. Although many mathematical methods have been proposed for noise removal and have been observed to be successful to some extent, they still lack of providing satisfactory performance.
With the development and popularization of artificial intelligence and deep learning networks, its use in the field of medical image processing has also increased. In this context, deep learning networks have been proposed as an alternative to mathematical regularizers as a denoiser in medical images. Deep learning networks perform their operations by establishing connections between the given task and the ground truths targeted during learning activities. For this reason, they need ground truths. However, ground truths are difficult parameters to obtain in many real-world problems. To overcome this difficulty, unsupervised network models have been proposed. There are different approaches as learning models for unsupervised network models. One of them aims to learn by benchmarking over different variations of a given task. Another one is the blind spot network structure. This structure aims to generate artificial variations based on a single input and to form the target with the data it diversifies. There are certain limitations for both approaches. The first one is that it is difficult to capture the same scene with different parameters, such as medical imaging and motion imaging, while the difficulty for the other method is the low success rate. Therefore, in order to provide a solution based on the first approach, this study aims to obtain a unsupervised denoising neural network that aims to remove noise from DBT images based on a network that has proven itself in removing noise from real-world images. 
 
As a solution to the need to recapture a scene with different parameters, the proposed method aims to obtain image pairs through sub-sampling sets from the scene and use these pairs as training input. Thus, noise removal can be performed on a single noisy image. Two different datasets are used to analyse the performance of the proposed method. The first dataset is the BSC-DBT (Breast Cancer Screening - Digital Breast Tomosynthesis) dataset from the cancer image archive. This dataset contains 5060 patients labelled as normal, action required, biopsy proven benign and biopsy proven cancer. Images were used in slices in 2D. To be equal from each category and total data was determined to be 70% training data, 15% validation data and 15% test data. In addition to this dataset, DBT-Phantoms dataset was used. The dataset was organized as 70% training data, 15% validation data and 15% test data, with a total of 2148 data and two-dimensional slice images. Since Gaussian and Poisson noises are the most prominent noise shapes due to low dose, these two noises were chosen as the noise removal targets and the training was completed by setting the training parameters to remove these noises. The basic parameter of Gaussian noise is the standard deviation of the region of interest (ROI). For this reason, to calculate the standard deviation of the ROI over the images, a 10% of the data set was randomly selected from the entire data set by sampling method. The standard deviations of the selected data were calculated and averaged. At the same time, the standard deviation of the averaged standard deviation data was calculated. Thus, a stable data with a mean of 25.3 standard deviations and a standard deviation of 1.19 was obtained and the training parameter (standard deviation) was set to 25. The basic parameter for Poisson noise is lambda (), which is the average of the pixel values of the region of interest. With the method applied for Gaussian,  was determined for Poisson noise and the average lambda value was 30.8 with a standard deviation of 1.27. For this reason, it was applied as 30 in the training data. In order to make the comparison fair and to simulate real-world problems well, in addition to the fixed value training, a training set with a normal distribution of noise levels was also created. For this training set, the parameters used in the constant value training were set so that the peak point was set to be the parameters used in the constant value training and they were distributed between 5 and 50 values. Thus, when comparisons were made, tables were created based on constant value constant noise, constant value distributed noise, distributed value constant noise and distributed value distributed noise values.
 Contrast-to-noise ratio (CNR), peak signal to noise ratio (PSNR) and structural similarity index (SSIM) metrics were used to evaluate the success of the proposed model. While SSIM is preferred because it allows the closest digital comparison to human perception, CNR is preferred because of the high importance of contrast resolution in medical images. PSNR is a good visual signal analysis method. The parameter values for the metrics were analysed based on the average sigma and lambda values as mentioned. During the study, the success of the mathematical regularizer method Block-matched three-dimensional filtering (BM3D) and the proposed model was compared on the metrics. The comparison was made by training the network with both fixed noise parameters and parameters varying over a normal distribution. In addition, the performance of the network trained with fixed parameters and sigma and lambda values that the training data is normally distributed and BM3D is tested for varying noise conditions. 

As a result of the study, the proposed unsupervised denoiser network was found to be superior to the BM3D mathematical regularizers method with the metrics determined. Considering the data acquisition costs and processing capabilities, the proposed model is quite promising.