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Research article

Thyroid nodule segmentation in ultrasound images using U-Net with ResNet encoder: achieving state-of-the-art performance on all public datasets

  • Received: 27 September 2024 Revised: 10 March 2025 Accepted: 20 March 2025 Published: 03 April 2025
  • Ultrasound imaging plays a vital role in evaluating thyroid nodules, aiding in the assessment of malignancy risk, monitoring size progression, and serving as a guiding tool for thyroid nodule biopsies. Computer-aided diagnosis (CAD) systems have emerged to assist in diagnosing thyroid lesions, reducing unnecessary biopsies, and contributing to the overall improvement of diagnostic accuracy. The segmentation process plays a crucial role in CAD systems because it marks the region of interest. If segmentation were sufficiently accurate, then it would improve the entire diagnostic process and bring CAD systems closer to routine clinical practice. As far as we know, there are currently only three publicly available datasets of ultrasound images of the thyroid gland that can be used for the purpose of thyroid nodules segmentation. The Thyroid Digital Image Database (TDID) is a long-standing benchmark dataset but faces limitations due to the data ambiguities. The TN3K dataset is more robust than TDID, and the Thyroid Ultrasound Cine-clip dataset offers recent alternatives. In this paper, we implemented a deep learning segmentation model based on UNet with a ResNet encoder. We trained this model on all available data and evaluated it on the TN3K test set. The achieved results for the Dice score, IoU score, accuracy, precision, and recall were 84.24%, 75.48%, 97.24%, 82.75%, and 88.98%, respectively. These results represent the most advanced state-of-the-art scores compared to previously published studies and demonstrate that UNet with a ResNet encoder has the capability to accurately segment thyroid nodules in ultrasound images.

    Citation: Antonin Prochazka, Jan Zeman. Thyroid nodule segmentation in ultrasound images using U-Net with ResNet encoder: achieving state-of-the-art performance on all public datasets[J]. AIMS Medical Science, 2025, 12(2): 124-144. doi: 10.3934/medsci.2025009

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  • Ultrasound imaging plays a vital role in evaluating thyroid nodules, aiding in the assessment of malignancy risk, monitoring size progression, and serving as a guiding tool for thyroid nodule biopsies. Computer-aided diagnosis (CAD) systems have emerged to assist in diagnosing thyroid lesions, reducing unnecessary biopsies, and contributing to the overall improvement of diagnostic accuracy. The segmentation process plays a crucial role in CAD systems because it marks the region of interest. If segmentation were sufficiently accurate, then it would improve the entire diagnostic process and bring CAD systems closer to routine clinical practice. As far as we know, there are currently only three publicly available datasets of ultrasound images of the thyroid gland that can be used for the purpose of thyroid nodules segmentation. The Thyroid Digital Image Database (TDID) is a long-standing benchmark dataset but faces limitations due to the data ambiguities. The TN3K dataset is more robust than TDID, and the Thyroid Ultrasound Cine-clip dataset offers recent alternatives. In this paper, we implemented a deep learning segmentation model based on UNet with a ResNet encoder. We trained this model on all available data and evaluated it on the TN3K test set. The achieved results for the Dice score, IoU score, accuracy, precision, and recall were 84.24%, 75.48%, 97.24%, 82.75%, and 88.98%, respectively. These results represent the most advanced state-of-the-art scores compared to previously published studies and demonstrate that UNet with a ResNet encoder has the capability to accurately segment thyroid nodules in ultrasound images.



    In this paper, we use G=(V(G),E(G)) to denote a graph with the vertex set V(G)={v1,v2,,vn} and the edge set E(G). Let e(G)=m=|E(G)| be the number of edges of the graph G. For any vi V(G), di=dvi=dG(vi) represents the degree of vi. Let (d1,d2,,dn) be the degree sequence of G, where d1d2dn. Let δ or δ(G) be the minimum degree of G, and NG(v) be the set of neighbors of a vertex v in G. The complete graph of order n is denoted by Kn, and the complete bipartite graph with the partite sets X and Y with |X|=m and |Y|=n is denoted by Km,n. The disjoint union of G and H, denoted by G+H, is the graph with vertex set V(G)V(H) and edge set E(G)(H). In particular, if G1=G2==Gk, then let kG1=G1+G2++Gk. Similarly, we use GH to denote the join of G and H. The complete graph on n1 vertices together with an isolated vertex v is denoted by Kn1+v.

    Let A(G)=[aij] be the adjacency matrix of G, where aij=1 if vi is adjacent to vj, aij=0 otherwise. We use μ(G) to denote the maximum eigenvalue of A(G), which is called the spectral radius of G. Let D(G) be the diagonal degree matrix of G, and Q(G)=D(G)+A(G) be the signless Laplacian matrix of G. Let q(G) be the maximum eigenvalue of Q(G), which is called the signless Laplacian spectral radius.

    We call the cycle (path) containing all vertices of G the Hamilton cycle (path) of G. If graph G contains a Hamiltonian cycle (path), then G is a Hamiltonian traceable graph. If for any positive integer k(3kn), G contains a cycle of length k, then G is called a pancyclic graph. Obviously, a bipartite graph is not a pancyclic graph. In addition, a Hamilton cycle graph is certainly Hamiltonian, but the converse is incorrect. Determining whether a given graph is a Hamilton graph or not is one of the most difficult classical problems in graph theory. In fact, it is an NPcomplete problem.

    In recent years, more and more worldwide researchers use the spectral theory of graphs to solve this problem. First, Fiedler and Nikiforov [1] established a sufficient condition on spectral radius for the existence of a graph with Hamilton paths and Hamilton cycles. Yu and Fan [2] used the spectral radius of the adjacency matrix or signless Laplacian matrix of the graph or its complement to give a sufficient condition for the graph to be Hamilton-connected. Lu et al. [3] gave the spectral sufficient conditions for a bipartite graph to be a Hamilton graph. Many scholars have studied similar problems under different spectral conditions since that time, see [4,5,6,7,8,9,10]. Recently, Yu et al. [11] has discussed for the first time the spectral sufficient conditions for a graph with a minimum degree δ(G)2 to be a pancyclic graph. In this paper, a new edge sufficient condition for a graph with a minimum degree δ(G)3 to be a pancyclic graph is established by using a similar method. Then, a sufficient condition on (signless Laplacian) spectral radius of a graph to be a pancyclic graph is given based on the edge sufficient condition.

    Given a graph G with order n, a vector XRn, is called to be defined on G, if there is a 1-1 mapping φ from V(G) to vector, simply written Xu=φ(u).

    X is defined on G if X is the eigenvector of A(G)(Q(G)). Let Xu denote the term of X corresponding to vertex u. One can find that when λ is an eigenvalue of G corresponding to the eigenvector, X if and only if X0,

    λXv=uNG(v)Xu,for eachvV(G). (2.1)

    The equation above is the eigen-equation of G. One can find when q is an signless Laplacian eigenvalue of G corresponding to the eigenvector X if and only if X0,

    |qdG(v)|λXv=uNG(v)Xu,for eachvV(G). (2.2)

    The equation above is the signless Laplacian eigen-equation of G.

    Lemma 2.1 [12] Let G be a graph with n vertices, and its degree sequence is d1d2dn. G is a pancyclic graph or bipartite graph if for all positive integers k there is always dnknk and dkk<n/2.

    Lemma 2.2 [13] Let G be a graph with n vertices and m edges, then

    μ(G)2mn+1,

    and the equality holds if and only if G=Kn or G=K1,n1.

    Lemma 2.3 [14] Let G be a graph with n vertices and m edges, then

    q(G)2mn1+n2,

    and the equal sign is true if and only if G=Kn or G=K1,n1 when G is connected. If G is not connected, the equal sign is true if and only if G=Kn1+v.

    Lemma 2.4 [15] Let G be a Hamiltonian graph and satisfy e(G)n2/4, then G is either a complete bipartite graph Kn2,n2 (where n is even), or a pancyclic graph.

    Lemma 2.5 [16] If a Hamiltonian graph G of order n satisfies

    e(G)>(n1)24+1,

    then G is a pancyclic graph or G is a bipartite graph.

    Theorem 3.1 Let G be a connected graph with n(n5) vertices and m edges, and its minimum degree δ(G)3. If

    m(n32)+9, (3.1)

    then G is a pancyclic graph or a bipartite graph or GNP1={(3K1+Kn6)K3, (4K1+K3)K4, (6K1+K2)K6, 9K1K8, 8K1K7, (K1+K1,7)K6, K2,8K5, K6(2K1+K1,6), K42K1(K2+K1,6), K5(K1+K1,6), (K2+K1,5)K5, (2K1K4)7K1, (K1+K2+K1,4)K5, (2K1+K1,5)K5, (K1+K26K1)K4, (K1,5+K2)2K1K3, 7K1K6, K5(2K1+K1,3+K2), K4[K1+K1(K1,4+K2)], K4(K1+2K16K1), K3K3,7, K5(3K1+K1,4), K32K1(K1+K1,4+K2), K4[K1(K1+K1,5)+K1], K32K1(2K1+K1,5), (5K1+K2)K5, K4(K1(4K1+K2)+K1), 7K1K5, (5K1+K2)2K1K3, 6K1K5, K4(K1,4+K2), K4(K1+K1,5), K3K2,6, K4(K1+K1,3+K2), K4(2K1+K1,4), K3[(K25K1)+K1], K22K1(K1,4+K2), K4(K2+K1,2+2K1), K3(K1+K2,5), K2K3,6, K4(K1,3+3K1), K22K1(K1+K1,3+K2), [K1(K1,4+K1)+K1]K3, K22K1(2K1+K1,4), (4K1+K2)K4, K3[K1+K1(3K1+K2)], 6K1K4, (4K1+K2)2K1K2, 5K1K4, (K1,3+K2)K3, (K1+K1,4)K3, 5K12K1K2, (K1+K1,2+K2)K3, (2K1+K1,3)K3, (K1,3+K2)2K1K1}.

    Proof: Suppose that G is neither a pancyclic graph nor a bipartite graph. According to Lemma 2.1, there is a positive integer k makes 3dkk<n/2 and dnknk1 hold at the same time. Then we have

    2m=Σki=1di+Σnki=k+1di+Σni=nk+1dik2+(n2k)(nk1)+k(n1)=n2n+3k2+(12n)k=2(n32)+18(k3)(2n3k10),

    thus

    m(n32)+9(k3)(2n3k10)2. (3.2)

    Since

    (n32)+9m(n32)+9(k3)(2n3k10)2, (3.3)

    thus (k3)(2n3k10)0. Next, the following two cases are discussed.

    Case 1 Assume that (k3)(2n3k10)=0, i.e., k=3 or 2n3k10=0. All the above inequalities are equal and m=(n32)+9.

    Case 1.1 If k=3, then G is a graph with d1=d2=d3=3,d4=d5==dn3=n4,dn2=dn1=dn=n1. Three vertices with degree n1 must be connected to other vertices, then a K3 is obtained. The three vertices with degree 3 are not connected with other vertices, and a 3K1 is obtained. The remaining n6 vertices with degree n4 must be connected to each other to ensure that the degree is n4, so they induce a Kn6. According to the above analysis, the graph G is (3K1+Kn6)K3.

    Case 1.2 If 2n3k10=0, then we get n19 because 2n103=k<n2, and hence n=11, k=4, or n=14, k=6, or n=17, k=8. The corresponding permissible graphic sequences are (4,4,4,4,6,6,6,10,10,10,10), (6,6,6,6,6,6,7,7,13,13,13,13,13,13), (8,8,8,8,8,8,8,8,8,16,16,16,16,16,16,16,16). Analysis shows that the graphs G are (4K1+K3)K4, (6K1+K2)K6 and 9K1K8 respectively.

    Case 2 Assume that (k3)(2n3k10)<0, i.e., k>3 and 2n3k10<0. Because 2n10<3k<32n, then, n=15,k=7 or n=13,k=6 or n=12,k=5 or n=11,k=5 or n=10,k=4 or n=9,k=4.

    Case 2.1 If n=15, k=7. According to equation (3.3), we have 150Σ15i=1di154. All possible degree sequences and their corresponding graphs are shown in Table 1.

    Table 1.  The degree sequences and corresponding graphs of Case 2.1.
    Degree sum No. The degree sequence The graph
    154 1 (7, 7, 7, 7, 7, 7, 7, 7, 14, 14, 14, 14, 14, 14, 14) G=8K1K7
    152 2 (7, 7, 7, 7, 7, 7, 7, 7, 12, 14, 14, 14, 14, 14, 14) G=K6(K2+K1,6)
    3 (6, 7, 7, 7, 7, 7, 7, 7, 13, 14, 14, 14, 14, 14, 14) G=(K1+K1,7)K6
    4 (7, 7, 7, 7, 7, 7, 7, 7, 13, 13, 14, 14, 14, 14, 14) G=K2,8K5
    150 5 (7, 7, 7, 7, 7, 7, 7, 7, 10, 14, 14, 14, 14, 14, 14) G=K6(2K2+K1,4)
    6 (6, 7, 7, 7, 7, 7, 7, 7, 11, 14, 14, 14, 14, 14, 14) G=(K1+K1,5+K2)K6
    7 (6, 6, 7, 7, 7, 7, 7, 7, 12, 14, 14, 14, 14, 14, 14) G=K6(2K1+K1,6)
    8 (7, 7, 7, 7, 7, 7, 7, 7, 12, 13, 13, 14, 14, 14, 14) G=K42K1(K2+K1,6)

     | Show Table
    DownLoad: CSV

    Case 2.2 If n=13, k=6. According to equation (3.3), we have 108Σ13i=1di114. All possible degree sequences and their corresponding graphs are shown in Table 2.

    Table 2.  The degree sequences and corresponding graphs of Case 2.2.
    Degree sum No. The degree sequence The graph
    114 1 (6, 6, 6, 6, 6, 6, 6, 12, 12, 12, 12, 12, 12) G=7K1K6
    112 2 (5, 6, 6, 6, 6, 6, 6, 11, 12, 12, 12, 12, 12) G=K5(K1+K1,6)
    3 (6, 6, 6, 6, 6, 6, 6, 10, 12, 12, 12, 12, 12) G=(K2+K1,5)K5
    4 (6, 6, 6, 6, 6, 6, 6, 11, 11, 12, 12, 12, 12) G=(2K1K4)7K1
    110 5 (6, 6, 6, 6, 6, 6, 6, 8, 12, 12, 12, 12, 12) G=(2K2+K1,3)k5
    6 (5, 6, 6, 6, 6, 6, 6, 9, 12, 12, 12, 12, 12) G=(K1+K2+K1,4)K5
    7 (5, 5, 6, 6, 6, 6, 6, 10, 12, 12, 12, 12, 12) G=(2K1+K1,5)K5
    8 (4, 6, 6, 6, 6, 6, 6, 11, 11, 12, 12, 12, 12) G=(K1+K26K1)K4
    9 (6, 6, 6, 6, 6, 6, 6, 10, 11, 11, 12, 12, 12) G=(K1,5+K2)2K1K3
    108 10 (6, 6, 6, 6, 6, 6, 6, 6, 12, 12, 12, 12, 12) G=4K2K5
    11 (5, 6, 6, 6, 6, 6, 6, 7, 12, 12, 12, 12, 12) G=(K1+K1,2+2K2)K5
    12 (6, 6, 6, 6, 6, 6, 6, 9, 9, 12, 12, 12, 12) G=K4(K24K1+K3)
    13 (5, 5, 6, 6, 6, 6, 6, 8, 12, 12, 12, 12, 12) G=K5(2K1+K1,3+K2)
    14 (6, 6, 6, 6, 6, 6, 6, 8, 11, 11, 12, 12, 12) G=K32K1(K1,3+2K2)
    15 (4, 6, 6, 6, 6, 6, 6, 9, 11, 12, 12, 12, 12) G=K4[K1+K1(K1,4+K2)]
    16 (4, 6, 6, 6, 6, 6, 6, 10, 10, 12, 12, 12, 12) G=K4(K1+2K16K1)
    17 (6, 6, 6, 6, 6, 6, 6, 10, 10, 10, 12, 12, 12) G=K3K3,7
    18 (5, 5, 5, 6, 6, 6, 6, 9, 12, 12, 12, 12, 12) G=K5(3K1+K1,4)
    19 (5, 6, 6, 6, 6, 6, 6, 9, 11, 11, 12, 12, 12) G=K32K1(K1+K1,4+K2)
    20 (5, 5, 6, 6, 6, 6, 6, 10, 10, 12, 12, 12, 12) G=K4(K25K1+K2)
    21 (5, 5, 6, 6, 6, 6, 6, 10, 10, 12, 12, 12, 12) G=K4(K1(K1+K1,5)+K1)
    22 (5, 5, 6, 6, 6, 6, 6, 10, 10, 12, 12, 12, 12) G=K32K1(2K1+K1,5)

     | Show Table
    DownLoad: CSV

    Case 2.3 If n=12, k=5. According to equation (3.3), we have 90Σ12i=1di92. All possible degree sequences and their corresponding graphs are shown in Table 3.

    Table 3.  The degree sequences and corresponding graphs of Case 2.3.
    Degree sum No. The degree sequence The graph
    92 1 (5, 5, 5, 5, 5, 6, 6, 11, 11, 11, 11, 11) G=(5K1+K2)K5
    90 2 (4, 5, 5, 5, 5, 6, 6, 10, 11, 11, 11, 11) G=K4(K1(4K1+K2)+K1)
    3 (5, 5, 5, 5, 5, 5, 5, 11, 11, 11, 11, 11) G=7K1K5
    4 (5, 5, 5, 5, 5, 6, 6, 10, 10, 11, 11, 11) G=(5K1+K2)2K1K3

     | Show Table
    DownLoad: CSV

    Case 2.4 If n=11, k=5. According to equation (3.3), we have 74Σ11i=1di80. All possible degree sequences and their corresponding graphs are shown in Table 4.

    Table 4.  The degree sequences and corresponding graphs of Case 2.4.
    Degree sum No. The degree sequence The graph
    80 1 (5, 5, 5, 5, 5, 5, 10, 10, 10, 10, 10) G=6K1K5
    78 2 (5, 5, 5, 5, 5, 5, 8, 10, 10, 10, 10) G=K4(K1,4+K2)
    3 (4, 5, 5, 5, 5, 5, 9, 10, 10, 10, 10) G=(K1+K1,5)K4
    4 (5, 5, 5, 5, 5, 5, 9, 9, 10, 10, 10) G=K3K2,6
    76 5 (5, 5, 5, 5, 5, 5, 6, 10, 10, 10, 10) G=K4(K1,2+2K2)
    6 (4, 5, 5, 5, 5, 5, 7, 10, 10, 10, 10) G=(K1+K1,3+K2)K4
    7 (4, 4, 5, 5, 5, 5, 8, 10, 10, 10, 10) G=K4(2K1+K1,4)
    8 (3, 5, 5, 5, 5, 5, 9, 9, 10, 10, 10) G=K3((K25K1)+K1)
    9 (5, 5, 5, 5, 5, 5, 8, 9, 9, 10, 10) G=K22K1(K2+K1,4)
    74 10 (4, 5, 5, 5, 5, 5, 5, 10, 10, 10, 10) G=(K1+3K2)K4
    11 (4, 4, 5, 5, 5, 5, 6, 10, 10, 10, 10) G=K4(K2+K1,2+2K1)
    12 (5, 5, 5, 5, 5, 5, 6, 9, 9, 10, 10) G=(K1,2+2K2)2K1K2
    13 (3, 5, 5, 5, 5, 5, 8, 8, 10, 10, 10) G=K3(K1+K2,5)
    14 (5, 5, 5, 5, 5, 5, 8, 8, 8, 10, 10) G=K2K3,6
    15 (4, 4, 4, 5, 5, 5, 7, 10, 10, 10, 10) G=K4(K1,3+3K1)
    16 (4, 5, 5, 5, 5, 5, 7, 9, 9, 10, 10) G=K22K1(K1+K1,3+K2)
    17 (3, 4, 5, 5, 5, 5, 8, 9, 10, 10, 10) G=(K1(K1,4+K1)+K1)K3
    18 (4, 4, 5, 5, 5, 5, 8, 9, 9, 10, 10) G=K22K1(2K1+K1,4)

     | Show Table
    DownLoad: CSV

    Case 2.5 If n=10, k=4. According to equation (3.3), we have 60Σ10i=1di62. All possible degree sequences and their corresponding graphs are shown in Table 5.

    Table 5.  The degree sequences and corresponding graphs of Case 2.5.
    Degree sum No. The degree sequence The graph
    62 1 (4, 4, 4, 4, 5, 5, 9, 9, 9, 9) G=(4K1+K2)K4
    60 2 (4, 4, 4, 4, 5, 5, 7, 9, 9, 9) G=K3(K1(2K1+K2)+K2)
    3 (3, 4, 4, 4, 5, 5, 8, 9, 9, 9) G=K3(K1+K1(3K1+K2))
    4 (4, 4, 4, 4, 4, 4, 9, 9, 9, 9) G=6K1K4
    5 (4, 4, 4, 4, 5, 5, 8, 8, 9, 9) G=(4K1+K2)2K1K2

     | Show Table
    DownLoad: CSV

    Case 2.6 If n=9, k=4. According to equation (3.3), we have 48Σ9i=1di52. All possible degree sequences and their corresponding graphs are shown in Table 6.

    Table 6.  The degree sequences and corresponding graphs of Case 2.6.
    Degree sum No. The degree sequence The graph
    52 1 (4, 4, 4, 4, 4, 8, 8, 8, 8) G=5K1K4
    50 2 (4, 4, 4, 4, 4, 6, 8, 8, 8) G=(K1,3+K2)K3
    3 (3, 4, 4, 4, 4, 7, 8, 8, 8) G=(K1+K1,4)K3
    4 (4, 4, 4, 4, 4, 7, 7, 8, 8) G=5K12K1K2
    48 5 (4, 4, 4, 4, 4, 4, 8, 8, 8) G=3K2K3
    6 (3, 4, 4, 4, 4, 5, 8, 8, 8) G=(K1+K1,2+K2)K3
    7 (3, 3, 4, 4, 4, 6, 8, 8, 8) G=(2K1+K1,3)K3
    8 (4, 4, 4, 4, 4, 6, 7, 7, 8) G=(K1,3+K2)2K1K1

     | Show Table
    DownLoad: CSV

    Next, we consider the pancyclicity of these graphs. For convenience, we put the maximum length cycle L(G) of G in the appendix. In the following description, when it comes to the description of the graphs, we will use the sequence number of the graphs in the appendix.

    As can be seen from the appendix, according to Lemma 2.5, all the graphs of No.5-18 in the appendix are pancyclic. Others belong to NP1 and they are neither pancyclic nor bipartite.

    The proof is complete.

    Theorem 3.2 Let G be a connected graph with n(n5) vertices and m edges, and its minimum degree δ(G)3. If

    μ(G)n28n+31,

    then G is a pancyclic graph or a bipartite graph.

    Proof: Suppose that G is neither a pancyclic graph nor a bipartite graph. Because a complete graph is a pancyclic graph and δ(K1,n1)=1, then G cannot be a complete graph or K1,n1. In such a situation, the equation in Lemma 2.2 is invalid. By Lemma 2.2, n28n+31μ(G)<2mn+1, i.e., m>(n32)+9.

    By Theorem 3.1, G is a pancyclic graph or a bipartite graph or GNP1. It can be calculated that (3K1+Kn6)K3, (4K1+K3)K4, (6K1+K2)K6, 9K1K8 satisfy m=(n32)+9, which is contradiction. Next, we will study whether the graphs of No.19-69 satisfy the spectral radius condition in the Theorem 3.2. Note that GNP2 and NP2 is the set of graphs of No.19-69 in the appendix.

    Take (2K1K4)7K1 as an example:

    Let X=(X1,X2,,X13)T be the eigenvector corresponding to μ(G), where Xi(1i2) corresponds to the vertex of degree 11, Xi(3i6) corresponds to the vertex of degree 12 and Xi(7i13) corresponds to the vertex of degree 6. By eigen-equation (2.1), then we have

    {X1=X2,X3=X4=X5=X6,X7=X8=X9=X10=X11=X12=X13μ(G)X1=4X3+7X7μ(G)X3=2X1+3X3+7X7μ(G)X7=2X1+4X3

    Transform the above equations into the matrix equation (A(G)μ(G)I)X=0, where X=(X1,X3,X7)T and

    A(G)=(047237240).

    Let f(x)=|xIA(G)|=x33x250x70, then the maximum root of f(x)=0 is μ(G)=9.235. It can be calculated that μ(G)<1328×13+31, which is contradiction. The remaining graphs are studied in the same way, and the results are shown in Table 7.

    Table 7.  The spectral radius and the signless Laplacian spectral radius of G.
    G μ(G) (n28n+31) q(G) 24n1+2n8
    No.19 11.0623 11.6619 23.4121 23.71429
    No.20 10.9484 11.6619 23.2009 23.71429
    No.21 10.8783 11.6619 21.9831 23.71429
    No.22 10.8374 11.6619 23.0178 23.71429
    No.23 10.7164 11.6619 22.6516 23.71429
    No.24 9.3157 9.7980 19.7584 20
    No.25 9.2660 9.7980 19.6332 20
    No.26 9.2350 9.7980 19.5281 20
    No.27 9.1467 9.7980 19.4616 20
    No.28 9.1884 9.7980 19.5522 20
    No.29 9.2049 9.7980 19.5364 20
    No.30 9.0474 9.7980 19.1235 20
    No.31 9.4462 9.7980 20 20
    No.32 9.0325 9.7980 19.3143 20
    No.33 9.0301 9.7980 19.2240 20
    No.34 9.0047 9.7980 19.1492 20
    No.35 8.8422 9.7980 18.7149 20
    No.36 9.0652 9.7980 19.3759 20
    No.37 8.9237 9.7980 18.9375 20
    No.38 9.0735 9.7980 19.3203 20
    No.39 8.9678 9.7980 19.0391 20
    No.40 8.3530 8.888194 17.8639 18.18182
    No.41 8.2145 8.888194 17.5985 18.18182
    No.42 8.2450 8.888194 17.7460 18.18182
    No.43 8.1124 8.888194 17.3128 18.18182
    No.44 7.8310 8 16.5887 16.4
    No.45 7.6196 8 16.1652 16.4
    No.46 7.6779 8 16.3062 16.4
    No.47 7.5826 8 16.0352 16.4
    No.48 7.4816 8 15.9748 16.4
    No.49 7.5284 8 16.0698 16.4
    No.50 7.5528 8 16.0487 16.4
    No.51 7.3589 8 15.5484 16.4
    No.52 7.3507 8 15.8151 16.4
    No.53 7.3171 8 15.6034 16.4
    No.54 7.1207 8 15.0772 16.4
    No.55 7.3840 8 15.8721 16.4
    No.56 7.2137 8 15.3351 16.4
    No.57 7.3968 8 15.7967 16.4
    No.58 7.2647 8 15.4474 16.4
    No.59 6.7573 7.1414 14.4721 14.6667
    No.60 6.5955 7.1414 14.1578 14.6667
    No.61 6.6235 7.1414 14.3246 14.6667
    No.62 6.4681 7.1414 13.8041 14.6667
    No.63 6.2170 6.3246 13.1789 13
    No.64 5.9612 6.3246 12.6769 13
    No.65 6.0322 6.3246 12.8381 13
    No.66 5.9150 6.3246 12.5052 13
    No.67 5.7980 6.3246 12.4641 13
    No.68 5.8503 6.3246 12.5601 13
    No.69 5.6362 6.3246 11.8807 13

     | Show Table
    DownLoad: CSV

    From Table 7, all graphs in NP2 satisfy μ(G)<n28n+31, a contradiction. The proof is complete.

    Theorem 3.3 Let G be a connected graph with n(n5) vertices and minimum degree δ(G)3. If

    q(G)24n1+(2n8), (3.4)

    then G is a pancyclic graph or a bipartite graph or 7K1K6 or 6K1K5 or 5K1K4.

    Proof: Suppose that G is neither a pancyclic graph nor a bipartite graph. Because a complete graph is a pancyclic graph and δ(K1,n1)=1, then G cannot be a complete graph or K1,n1. In such a situation, the equation in Lemma 2.3 is invalid. By Lemma 2.3, 24n1+2n8q(G)<2mn1+n2, i.e., m>(n32)+9.

    By Theorem 3.1, G is a pancyclic graph or a bipartite graph or GNP1. It can be calculated that the graphs of (3K1+Kn6)K3, (4K1+K3)K4, (6K1+K2)K6, 9K1K8 satisfy m=(n32)+9, which is contradiction. Next, we will study whether the graphs of No.19-69 satisfy the signless Laplacian spectral radius condition in the Theorem 3.3. Note that GNP2.

    Take (2K1K4)7K1 as an example:

    Let X=(X1,X2,,X13)T be the eigenvector corresponding to μ(G), where Xi(1i2) corresponds to the vertex of degree 11, Xi(3i6) corresponds to the vertex of degree 12 and Xi(7i13) corresponds to the vertex of degree 6. By eigen-equation (2.2), then we have

    {X1=X2,X3=X4=X5=X6,X7=X8=X9=X10=X11=X12=X13(q(G)11)X1=4X3+7X7(q(G)12)X3=2X1+3X3+7X7(q(G)6)X7=2X1+4X3

    Transform the above equations into the matrix equation (Q(G)q(G)I)X=0, where X=(X1,X3,X7)T and

    Q(G)=(11472157246).

    Let f(x)=|xIQ(G)|=x332x2+271x536, then the maximum root of g(x)=0 is q(G)=19.528. It can be calculated that q(G)<24131+2×138, which is contradiction. The remaining graphs are studied in the same way, and the results are shown in Table 7.

    From Table7, all graphs in NP2 except G=7K1K6 and G=6K1K5 and G=5K1K4 satisfy q(G)<24n1+2n8, a contradiction.

    The proof is complete.

    This work is supported by the Natural Science Foundation of China(11871077), the NSF of Anhui Province(1808085MA04), the NSF of Department of Education of Anhui Province(KJ2017A362), and the NSF of Anhui Province(KJ2018A0964), the Deanship of Scientific Research (DSR) at King Abdulaziz University under grant no (RG-19-135-38), the 2019 Teaching and research project of Hefei Preschool Education College (hyzzd2019003).

    The author declare that he has no competing interest.

    Table  .  The maximum length cycle L(G) of G.
    No. G Order L(G) e(G) (n1)24+1
    1 (3K1+Kn6)K3 n Cn1 n27n+302 (n1)24+1
    2 (4K1+K3)K4 11 C10 37 26
    3 (6K1+K2)K6 14 C13 64 43.25
    4 9K1K8 17 C16 100 65
    5 K6(K2+K1,6) 15 C15 76 50
    6 K6(2K2+K1,4) 15 C15 75 50
    7 (K1+K1,5+K2)K6 15 C15 75 50
    8 (2K2+K1,3)K5 13 C13 55 37
    9 4K2K5 13 C13 54 37
    10 (K1+K1,2+2K2)K5 13 C13 54 37
    11 K4(K24K1+K3) 13 C13 54 37
    12 K32K1(K1,3+2K2) 13 C13 54 37
    13 K4(K25K1+K2) 13 C13 54 37
    14 K4(K1,2+2K2) 11 C11 38 26
    15 (K1+3K2)K4 11 C11 37 26
    16 (K1,2+2K2)2K1K2 11 C11 37 26
    17 K3(K1(2K1+K2)+K2) 10 C10 30 21.25
    18 3K2K3 9 C9 24 17
    19 8K1K7 15 C14 77 50
    20 (K1+K1,7)K6 15 C14 76 50
    21 K2,8K5 15 C14 76 50
    22 K6(2K1+K1,6) 15 C14 75 50
    23 K42K1(K2+K1,6) 15 C11 75 50
    24 K5(K1+K1,6) 13 C11 56 37
    25 (K2+K1,5)K5 13 C12 56 37
    26 (2K1K4)7K1 13 C12 56 37
    27 (K1+K2+K1,4)K5 13 C12 55 37
    28 (2K1+K1,5)K5 13 C11 55 37
    29 (K1+K26K1)K4 13 C11 54 37
    30 (K1,5+K2)2K1K3 13 C12 55 37
    31 7K1K6 13 C12 57 37
    32 K5(2K1+K1,3+K2) 13 C12 54 37
    33 K4[K1+K1(K1,4+K2)] 13 C12 54 37
    34 K4(K1+2K16K1) 13 C12 54 37
    35 K3K3,7 13 C12 54 37
    36 K5(3K1+K1,4) 13 C11 54 37
    37 K32K1(K1+K1,4+K2) 13 C12 54 37
    38 K4[K1(K1+K1,5)+K1] 13 C11 54 37
    39 K32K1(2K1+K1,5) 13 C11 54 37
    40 (5K1+K2)K5 12 C11 46 31.25
    41 K4(K1(4K1+K2)+K1) 12 C11 45 31.25
    42 7K1K5 12 C10 45 31.25
    43 (5K1+K2)2K1K3 12 C11 45 31.25
    44 6K1K5 11 C10 40 26
    45 K4(K1,4+K2) 11 C10 39 26
    46 K4(K1+K1,5) 11 C10 39 26
    47 K3K2,6 11 C10 39 26
    48 K4(K1+K1,3+K2) 11 C10 38 26
    49 K4(2K1+K1,4) 11 C9 38 26
    50 K3[(K25K1)+K1] 11 C10 38 26
    51 K22K1(K1,4+K2) 11 C10 38 26
    52 K4(K2+K1,2+2K1) 11 C10 37 26
    53 K3(K1+K2,5) 11 C10 37 26
    54 K2K3,6 11 C10 37 26
    55 K4(K1,3+3K1) 11 C9 37 26
    56 K22K1(K1+K1,3+K2) 11 C10 37 26
    57 [K1(K1,4+K1)+K1]K3 11 C9 37 26
    58 K22K1(2K1+K1,4) 11 C9 37 26
    59 (4K1+K2)K4 10 C9 31 21.25
    60 K3[K1+K1(3K1+K2)] 10 C9 30 21.25
    61 6K1K4 10 C8 30 21.25
    62 (4K1+K2)2K1K2 10 C9 30 21.25
    63 5K1K4 9 C8 26 17
    64 (K1,3+K2)K3 9 C8 25 17
    65 (K1+K1,4)K3 9 C7 25 17
    66 5K12K1K2 9 C8 25 17
    67 (K1+K1,2+K2)K3 9 C8 24 17
    68 (2K1+K1,3)K3 9 C7 24 17
    69 (K1,3+K2)2K1K1 10 C8 24 17

     | Show Table
    DownLoad: CSV

    Acknowledgments



    This research used data provided by the Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI). AIMI curated a publicly available imaging data repository containing clinical imaging and data from Stanford Health Care, the Stanford Children's Hospital, the University Healthcare Alliance and Packard Children's Health Alliance clinics provisioned for research use by the Stanford Medicine Research Data Repository (STARR).

    1 Available here: https://github.com/haifangong/TRFE-Net-for-thyroid-nodule-segmentation.

    2 Available here: http://cimalab.unal.edu.co/applications/thyroid/.

    3 Available here: https://stanfordaimi.azurewebsites.net/datasets/a72f2b02-7b53-4c5d-963c-d7253220bfd5.

    Conflict of interest



    The authors declare no conflict of interest.

    [1] Rahbari R, Zhang L, Kebebew E (2010) Thyroid cancer gender disparity. Future Oncol 6: 1771-1779. https://doi.org/10.2217/fon.10.127
    [2] Haugen BR, Alexander EK, Bible KC, et al. (2016) 2015 American Thyroid Association Management Guidelines for adult patients with thyroid nodules and differentiated thyroid cancer: the American Thyroid Association Guidelines task force on thyroid nodules and differentiated thyroid cancer. Thyroid 26: 1-133. https://doi.org/10.1089/thy.2015.0020
    [3] Tunbridge WM, Evered DC, Hall R, et al. (1977) The spectrum of thyroid disease in a community: the Whickham survey. Clin Endocrinol (Oxf) 7: 481-493. https://doi.org/10.1111/j.1365-2265.1977.tb01340.x
    [4] Tan GH, Gharib H (1997) Thyroid incidentalomas: management approaches to nonpalpable nodules discovered incidentally on thyroid imaging. Ann Intern Med 126: 226-231. https://doi.org/10.7326/0003-4819-126-3-199702010-00009
    [5] Guth S, Theune U, Aberle J, et al. (2009) Very high prevalence of thyroid nodules detected by high frequency (13 MHz) ultrasound examination. Eur J Clin Invest 39: 699-706. https://doi.org/10.1111/j.1365-2362.2009.02162.x
    [6] Remonti LR, Kramer CK, Leitao CB, et al. (2015) Thyroid ultrasound features and risk of carcinoma: a systematic review and meta-analysis of observational studies. Thyroid 25: 538-550. https://doi.org/10.1089/thy.2014.0353
    [7] Brito JP, Gionfriddo MR, Al Nofal A, et al. (2014) The accuracy of thyroid nodule ultrasound to predict thyroid cancer: systematic review and meta-analysis. J Clin Endocrinol Metab 99: 1253-1263. https://doi.org/10.1210/jc.2013-2928
    [8] Tessler FN, Middleton WD, Grant EG, et al. (2017) ACR Thyroid Imaging, Reporting and Data System (TI-RADS): white paper of the ACR TI-RADS committee. J Am Coll Radiol 14: 587-595. https://doi.org/10.1016/j.jacr.2017.01.046
    [9] Zhu YC, AlZoubi A, Jassim S, et al. (2021) A generic deep learning framework to classify thyroid and breast lesions in ultrasound images. Ultrasonics 110: 106300. https://doi.org/10.1016/j.ultras.2020.106300
    [10] Chi J, Walia E, Babyn P, et al. (2017) Thyroid nodule classification in ultrasound images by fine-tuning deep convolutional neural network. J Digit Imaging 30: 477-486. https://doi.org/10.1007/s10278-017-9997-y
    [11] Mei X, Dong X, Deyer T, et al. (2017) Thyroid nodule benignty prediction by deep feature extraction. 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE) . Washington, DC, USA: 241-245. https://doi.org/10.1109/BIBE.2017.00-48
    [12] Liu T, Xie S, Yu J, et al. (2017) Classification of thyroid nodules in ultrasound images using deep model based transfer learning and hybrid features. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . New Orleans, LA, USA: 919-923. https://doi.org/109/ICASSP.2017.7952290
    [13] Prochazka A, Gulati S, Holinka S, et al. (2019) Patch-based classification of thyroid nodules in ultrasound images using direction independent features extracted by two-threshold binary decomposition. Comput Med Imaging Graph 71: 9-18. https://doi.org/10.1016/j.compmedimag.2018.10.001
    [14] Prochazka A, Gulati S, Holinka S, et al. (2019) Classification of thyroid nodules in ultrasound images using direction-independent features extracted by two-threshold binary decomposition. Technol Cancer Res Treat 18. https://doi.org/10.1177/1533033819830748
    [15] Chen J, You H, Li K (2020) A review of thyroid gland segmentation and thyroid nodule segmentation methods for medical ultrasound images. Comput Methods Programs Biomed 185: 105329. https://doi.org/10.1016/j.cmpb.2020.105329
    [16] Gong H, Chen J, Chen G, et al. (2023) Thyroid region prior guided attention for ultrasound segmentation of thyroid nodules. Comput Biol Med 155: 106389. https://doi.org/10.1016/j.compbiomed.2022.106389
    [17] Pan H, Zhou Q, Latecki LJ (2021) SGUNET: Semantic guided UNET for thyroid nodule segmentation. 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) . Nice, France: 630-634. https://doi.org/10.1109/ISBI48211.2021.9434051
    [18] Li C, Du R, Luo Q, et al. (2023) A novel model of thyroid nodule segmentation for ultrasound images. Ultrasound Med Biol 49: 489-496. https://doi.org/10.1016/j.ultrasmedbio.2022.09.017
    [19] Wang R, Zhou H, Fu P, et al. (2023) A multiscale attentional unet model for automatic segmentation in medical ultrasound images. Ultrason Imaging 45: 159-174. https://doi.org/10.1177/01617346231169789
    [20] Pedraza L, Vargas C, Narvaez F, et al. (2015) An open access thyroid ultrasound-image Database. 10th International Symposium on Medical Information Processing and Analysis . https://doi.org/10.1117/12.2073532
    [21] Ma J, Wu F, Jiang T, et al. (2017) Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks. Int J Comput Assist Radiol Surg 12: 1895-1910. https://doi.org/10.1007/s11548-017-1649-7
    [22] Ying X, Yu Z, Yu R, et al. (2018) Thyroid nodule segmentation in ultrasound images based on cascaded convolutional neural network. Neural Information Processing . Cham: Springer 373-384. https://doi.org/10.1007/978-3-030-04224-0_32
    [23] Ronneberger O, Fischer P, Brox T (2015) U-Net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 . Cham: Springer 234-241. https://doi.org/10.1007/978-3-319-24574-4_28
    [24] Simonyan K, Zisserman A (2014). https://doi.org/10.48550/arXiv.1409.1556
    [25] Kumar V, Webb J, Gregory A, et al. (2020) Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. IEEE Access 8: 63482-63496. https://doi.org/10.1109/ACCESS.2020.2982390
    [26] Sun J, Li C, Lu Z, et al. (2022) TNSNet: Thyroid nodule segmentation in ultrasound imaging using soft shape supervision. Comput Meth Prog Bio 215: 106600. https://doi.org/10.1016/j.cmpb.2021.106600
    [27] Song R, Zhu C, Zhang L, et al. (2022) Dual-branch network via pseudo-label training for thyroid nodule detection in ultrasound image. Appl Intell 52: 11738-11754. https://doi.org/10.1007/s10489-021-02967-2
    [28] Gomes Ataide E, Agrawal S, Jauhari A, et al. (2021) Comparison of deep learning algorithms for semantic segmentation of ultrasound thyroid nodules. Curr Dir Biomed Eng 7: 879-882. https://doi.org/10.1515/cdbme-2021-2224
    [29] Niu K, Guo Z, Peng X, et al. (2022) P-ResUnet: Segmentation of brain tissue with Purified Residual Unet. Comput Biol Med 151: 106294. https://doi.org/10.1016/j.compbiomed.2022.106294
    [30] Diakogiannis FI, Waldner F, Caccetta P, et al. (2020) ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data. Isprs J Photogramm Remote Sens 162: 94-114. https://doi.org/10.1016/j.isprsjprs.2020.01.013
    [31] Wang JJ, Gao J, Ren JW, et al. (2021) DFP-ResUNet: Convolutional neural network with a dilated convolutional feature pyramid for multimodal brain tumor segmentation. Comput Meth Prog Biomed 208. https://doi.org/10.1016/j.cmpb.2021.106208
    [32] Morelli R, Clissa L, Amici R, et al. (2021) Automating cell counting in fluorescent microscopy through deep learning with c-ResUnet. Sci Rep 11. https://doi.org/10.1038/s41598-021-01929-5
    [33] Sabir MW, Khan Z, Saad NM, et al. (2022) Segmentation of liver tumor in CT scan using ResU-Net. Appl Sci 12. https://doi.org/10.3390/app12178650
    [34] Wang R, Shen H, Zhou M (2019) Ultrasound nerve segmentation of brachial plexus based on optimized ResU-Net. 2019 IEEE International Conference on Imaging Systems & Techniques (IST) . https://doi.org/10.1109/IST48021.2019.9010317
    [35] Cai L, Li Q, Zhang J, et al. (2023) Ultrasound image segmentation based on Transformer and U-Net with joint loss. PeerJ Comput Sci 9: e1638. https://doi.org/10.7717/peerj-cs.1638
    [36] Song SH, Han JH, Kim KS, et al. (2022) Deep-learning segmentation of ultrasound images for automated calculation of the hydronephrosis area to renal parenchyma ratio. Investig Clin Urol 63: 455-463. https://doi.org/10.4111/icu.20220085
    [37] Chen J, Lu Y, Yu Q, et al. Transunet: Transformers make strong encoders for medical image segmentation (2021). https://doi.org/10.48550/arXiv.2102.04306
    [38] Chouiha B, Amamra A (2021) Thyroid Nodules Recognition in ultrasound images based on ImageNet top-performing deep convolutional neural networks. Advances in Computing Systems and Applications . Cham: Springer 313-322. https://doi.org/10.1007/978-3-030-69418-0_28
    [39] Gomes Ataide EJ, Ponugoti N, Illanes A, et al. (2020) Thyroid nodule classification for physician decision support using machine learning-evaluated geometric and morphological features. Sensors 20. https://doi.org/10.3390/s20216110
    [40] Buslaev A, Iglovikov VI, Khvedchenya E, et al. (2020) Albumentations: Fast and flexible image augmentations. Information 11. https://doi.org/10.3390/info11020125
    [41] Loshchilov I, Hutter F Sgdr: Stochastic gradient descent with warm restarts (2016). https://doi.org/10.48550/arXiv.1608.03983
    [42] Gong H, Chen G, Wang R, et al. (2021) Multi-task learning for thyroid nodule segmentation with thyroid region prior. 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) . Nice, France: 257-261. https://doi.org/10.1109/ISBI48211.2021.9434087
    [43] Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39: 640-651. https://doi.org/10.1109/TPAMI.2016.2572683
    [44] Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39: 2481-2495. https://doi.org/10.1109/TPAMI.2016.2644615
    [45] Chen LC, Zhu Y, Papandreou G, et al. (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. Computer Vision – ECCV 2018 . Cham: Springer 833-851. https://doi.org/10.1007/978-3-030-01234-2_49
    [46] Feng S, Zhao H, Shi F, et al. (2020) CPFNet: Context pyramid fusion network for medical image segmentation. IEEE Trans Med Imaging 39: 3008-3018. https://doi.org/10.1109/TMI.2020.2983721
    [47] Brauer VFH, Eder P, Miehle K, et al. (2005) Interobserver variation for ultrasound determination of thyroid nodule volumes. Thyroid 15: 1169-1175. https://doi.org/10.1089/thy.2005.15.1169
    [48] Lee HJ, Yoon DY, Seo YL, et al. (2018) Intraobserver and interobserver variability in ultrasound measurements of thyroid nodules. J Ultrasound Med 37: 173-178. https://doi.org/10.1002/jum.14316
    [49] Özgen A, Erol C, Kaya A, et al. (1999) Interobserver and intraobserver variations in sonographic measurement of thyroid volume in children. Eur J Endocrinol 140: 328-331. https://doi.org/10.1530/eje.0.1400328
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