Derin öğrenme temelli nesne tespit yöntemleri kullanılarak insan sperm hücrelerinin tespiti

Öz

Kısırlık son 50 yılda dünya çapında önemli bir sağlık problemi haline gelmiştir. Dünyanın farklı bölgelerine göre oranı değişen bu problem ortalama olarak dünyadaki her on çiften birini etkilemektedir. Erkek kaynaklı kısırlık teşhisi, sperm kalitesi değerlendirilerek yapılır. Sperm kalitesi araştırılırken sperm sayısı, hareketi ve morfolojik yapısı değerlendirilir. Sperm hareketi ve sayımının analizi öncesinde sperm tespiti önemli bir adımdır. Bu çalışmada, oluşturulan özgün yeni semen video veri kümesi üzerinde Faster R-CNN ve YOLOv3 derin öğrenme yöntemleri kullanılarak otonom sperm tespiti gerçekleştirilmiştir. Çalışma kapsamında oluşturulan bu özgün veri kümesi, mikroskop altında semen örneklerinin cep telefonu yardımı ile elde edilen 10 hastaya ait semen videolarını ve içeriğindeki nesnelerin sperm ve sperm olmayan şeklindeki etiket bilgisini içermektedir. Analiz için hazırlanmış etiketli videolar hasta odaklı ve hasta bağımsız olmak üzere iki senaryo ile değerlendirilmiştir. İlk senaryomuzda etiketli sekiz video birleştirilerek Faster R-CNN ve YOLOv3 modelleri 3 farklı oranda oluşturulmuş veriler ile eğitilmiş ve test edilmiştir. İkinci senaryoda ise eğitilmiş her bir modelimiz daha önce eğitime hiç katılmamış iki video ile test edilmiştir. İkinci senaryomuzda, eğitimine hiç katılmamış videolar kullanılarak algılama performansları değerlendirilmiştir. Yapılan çalışmada YOLOv3 modeli ile bireysel videolarda %96, ortalama da ise %84,5 gibi mAP sperm tespit sonuçları elde edilmiştir. Sonuçlar nesne tespitinin doğruluğu ve eğitim süreleri gibi iki önemli kriter ile karşılaştırıldığında YOLOv3 yöntemi Faster R-CNN yönteminden daha başarılı olduğu gözlemlenmiştir.

Anahtar Kelimeler

• Kısırlık
• Video analizi
• Sperm tespiti
• YOLOv3
• Faster R-CNN

Detection of human sperm cells using deep learning-based object detection methods

Abstract

Infertility has become a significant health issue worldwide in the last 50 years. This issue, with varying rates across different regions of the world, affects approximately one out of every ten couples on average globally. Diagnosis of male-related infertility is conducted by evaluating sperm quality. When investigating sperm quality, factors such as sperm count, motility, and morphological structure are assessed. Detection of sperm before the analysis of sperm motility and count is an important step. In this study, autonomous sperm detection was carried out using deep learning methods, namely Faster R-CNN and YOLOv3, on a newly generated and unique semen video dataset. This distinct dataset, created within the scope of this study, includes semen videos from 10 patients obtained with the assistance of a mobile phone under a microscope. Videos contain label information that classifies objects as sperm and non-sperm. Labeled videos prepared for analysis were evaluated under two scenarios: patient-focused and patient-independent. In the first scenario, eight labeled videos were combined to train and test Faster R-CNN and YOLOv3 models in three different ratios. In the second scenario, each trained model was tested with two videos that had never been part of the training process. In this second scenario, detection performances were evaluated using videos that had not been involved in training. The study achieved sperm detection results of approximately 96% in individual videos using the YOLOv3 model and an average mAP of 84.5%. When compared against two significant criteria, object detection accuracy and training times, the YOLOv3 method was observed to be more successful than the Faster R-CNN method.

Keywords

• Infertility
• Video analysis
• Sperm detection
• YOLOv3
• Faster R-CNN

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