The use of deep learning to automate the segmentation of the skeleton from CT volumes of pigs1

Kvam, Johannes; Gangsei, Lars Erik; Kongsro, Jørgen; Solberg, Anne

doi:10.1093/tas/txy060

Cited by 12 publications

(11 citation statements)

References 26 publications

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“…YÖK Tez Merkezi'nden edinilen verilere göre; 2010-2020 yılları arasında görüntü işleme konusunda yazılan 60 doktora tezinden 24 tanesinin biyomedikal alanda olduğu tespit edilmiştir [13]. Ayrıca son yıllardaki literatür incelendiğinde ortopedi dalının yanında görüntü işleme konusunun göz hastalıkları, nöroloji, göğüs hastalıkları, dermatoloji, kardiyoloji gibi diğer tıp dallarında da uygulandığı görülmektedir [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29].…”

Section: Literatür çAlışmalarıunclassified

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Köpeklerdeki Uzun Kemiklerin Evrişimsel Sinir Ağları Kullanılarak Sınıflandırılması

Ergün¹,

Güney²,

Ergün³

2021

Fırat Üniversitesi Mühendislik Bilimleri Dergisi

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Son yılların en popüler konularından olan derin öğrenme, pek çok alanda olduğu gibi biyomedikal alanda da sıkça kullanılmaktadır. Çeşitli görüntüleme yöntemleri ile elde edilen görüntüler kullanılarak hastalık ve kırık tespiti, biyolojik veri kestirimi, doku ve organ bölütlemesi, eksik veri tamamlanması gibi nice uygulama derin öğrenme algoritmaları sayesinde başarılı bir şekilde gerçekleştirilmektedir. Ancak bahsi geçen uygulamaların çok büyük bir çoğunluğu beşeri hekimlikte yapılırken, veteriner tıp geri planda bırakılmıştır. Özellikle literatürde bu alandaki eksikliğin fark edilmesi bu çalışma konusunun en büyük motivasyon kaynağı olmuştur. Bu çalışmada, Ankara Büyükşehir Belediyesi Sokak Hayvanları Geçici Bakım Evi'nden alınan, köpeklere ait röntgenleri içeren geniş kapsamlı bir veri seti, derin öğrenme algoritmaları ile işlenmiştir. Amaç, köpeklere ait X-Ray görüntülerinden uzun kemiğin çeşidinin belirlenmesidir. Biyomedikal görüntü işleme alandaki pek çok çalışma gibi, bu çalışmada da Evrişimsel Sinir Ağları (Convolutional Neural Network, CNN) mimarileri kullanılmıştır. AlexNet, GoogLeNet ve VGG-19 derin öğrenme modelleri ile öğrenme aktarımı gerçekleştirilmiş, destek vektör makineleri (Support Vector Machines, SVM) ile sınıflandırma performansı test edilmiştir.

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Section: Literatür çAlışmalarıunclassified

“…Literatürde ender rastlanan hayvan görüntülerinin işlenmesine, domuzlar ile gerçekleştirilen bir çalışma örnek olarak verilebilir. Domuzların X-Ray görüntüleri ile iskelet yapıları CNN uygulanarak bölütlenmeye çalışılmış ve %95 gibi bir başarı elde edilmiştir [22].…”

Section: Literatür çAlışmalarıunclassified

Köpeklerdeki Uzun Kemiklerin Evrişimsel Sinir Ağları Kullanılarak Sınıflandırılması

Ergün¹,

Güney²,

Ergün³

2021

Fırat Üniversitesi Mühendislik Bilimleri Dergisi

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“…A few interdisciplinary studies integrate engineering and veterinary medicine. A study on pigs can be given as an example of animal image processing [6]. The aim is to develop a fully automated pig skeleton segmentation method from computed tomography (CT) scanning.…”

Section: Introductionmentioning

confidence: 99%

“…Applying a convolutional neural network (CNN), the researchers achieved 95% success and claimed that using deep learning methods increased robustness and reduced the need for manual control. According to the paper, the outcome of the image segmentation process can be used to improve pig breeding [6].…”

Section: Introductionmentioning

confidence: 99%

Classification of Canine Maturity and Bone Fracture Time Based on X-Ray Images of Long Bones

Ergün

Güney

2021

IEEE Access

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Veterinarians use X-rays for almost all examinations of clinical fractures to determine the appropriate treatment. Before treatment, vets need to know the date of the injury, type of the broken bone, and age of the dog. The maturity of the dog and the time of the fracture affects the approach to the fracture site, the surgical procedure and needed materials. This comprehensive study has three main goals: determining the maturity of the dogs (Task 1), dating fractures (Task 2), and finally, detecting fractures of the long bones in dogs (Task 3). The most popular deep neural networks are used: AlexNet, ResNet-50 and GoogLeNet. One of the most popular machine learning algorithms, support vector machines (SVM), is used for comparison. The performance of all sub-studies is evaluated using accuracy and F1 score. Each task has been successful with different network architecture. ResNet-50, AlexNet and GoogLeNet are the most successful algorithms for the three tasks, with F1 scores of 0.75, 0.80 and 0.88, respectively. Data augmentation is performed to make models more robust, and the F1 scores of the three tasks were 0.80, 0.81, and 0.89 using ResNet-50, which is the most successful model. This preliminary work can be developed into support tools for practicing veterinarians that will make a difference in the treatment of dogs with fractured bones. Considering the lack of work in this interdisciplinary field, this paper may lead to future studies.INDEX TERMS bone age, bone fractures, classification, convolutional neural networks, deep learning.

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“…The need for high-throughput phenotyping in animal breeding has accelerated the development of automated segmentation of computed tomography (CT) images [1][2][3] and the generation of new precision phenotypes based on the morphology of animals [4]. Detailed 3D models would be useful for the development of new indicators for economically important traits that are not directly measurable on selection candidates, such as longevity and robustness.…”

Section: Introductionmentioning

confidence: 99%

Modelling the shape of the pig scapula

Nordbø¹

2020

Genet Sel Evol

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Background: The shape of pig scapula is complex and is important for sow robustness and health. To better understand the relationship between 3D shape of the scapula and functional traits, it is necessary to build a model that explains most of the morphological variation between animals. This requires point correspondence, i.e. a map that explains which points represent the same piece of tissue among individuals. The objective of this study was to further develop an automated computational pipeline for the segmentation of computed tomography (CT) scans to incorporate 3D modelling of the scapula, and to develop a genetic prediction model for 3D morphology. Results: The surface voxels of the scapula were identified on 2143 CT-scanned pigs, and point correspondence was established by predicting the coordinates of 1234 semi-landmarks on each animal, using the coherent point drift algorithm. A subsequent principal component analysis showed that the first 10 principal components covered more than 80% of the total variation in 3D shape of the scapula. Using principal component scores as phenotypes in a genetic model, estimates of heritability ranged from 0.4 to 0.8 (with standard errors from 0.07 to 0.08). To validate the entire computational pipeline, a statistical model was trained to predict scapula shape based on marker genotype data. The mean prediction reliability averaged over the whole scapula was equal to 0.18 (standard deviation = 0.05) with a higher reliability in convex than in concave regions. Conclusions: Estimates of heritability of the principal components were high and indicated that the computational pipeline that processes CT data to principal component phenotypes was associated with little error. Furthermore, we showed that it is possible to predict the 3D shape of scapula based on marker genotype data. Taken together, these results show that the proposed computational pipeline closes the gap between a point cloud representing the shape of an animal and its underlying genetic components.

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The use of deep learning to automate the segmentation of the skeleton from CT volumes of pigs1

Cited by 12 publications

References 26 publications

Köpeklerdeki Uzun Kemiklerin Evrişimsel Sinir Ağları Kullanılarak Sınıflandırılması

Köpeklerdeki Uzun Kemiklerin Evrişimsel Sinir Ağları Kullanılarak Sınıflandırılması

Classification of Canine Maturity and Bone Fracture Time Based on X-Ray Images of Long Bones

Modelling the shape of the pig scapula

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