Loading [MathJax]/jax/output/SVG/jax.js
Research article Special Issues

TS-GCN: A novel tumor segmentation method integrating transformer and GCN

  • Received: 15 May 2023 Revised: 29 May 2023 Accepted: 30 May 2023 Published: 21 September 2023
  • As one of the critical branches of medical image processing, the task of segmentation of breast cancer tumors is of great importance for planning surgical interventions, radiotherapy and chemotherapy. Breast cancer tumor segmentation faces several challenges, including the inherent complexity and heterogeneity of breast tissue, the presence of various imaging artifacts and noise in medical images, low contrast between the tumor region and healthy tissue, and inconsistent size of the tumor region. Furthermore, the existing segmentation methods may not fully capture the rich spatial and contextual information in small-sized regions in breast images, leading to suboptimal performance. In this paper, we propose a novel breast tumor segmentation method, called the transformer and graph convolutional neural (TS-GCN) network, for medical imaging analysis. Specifically, we designed a feature aggregation network to fuse the features extracted from the transformer, GCN and convolutional neural network (CNN) networks. The CNN extract network is designed for the image's local deep feature, and the transformer and GCN networks can better capture the spatial and context dependencies among pixels in images. By leveraging the strengths of three feature extraction networks, our method achieved superior segmentation performance on the BUSI dataset and dataset B. The TS-GCN showed the best performance on several indexes, with Acc of 0.9373, Dice of 0.9058, IoU of 0.7634, F1 score of 0.9338, and AUC of 0.9692, which outperforms other state-of-the-art methods. The research of this segmentation method provides a promising future for medical image analysis and diagnosis of other diseases.

    Citation: Haiyan Song, Cuihong Liu, Shengnan Li, Peixiao Zhang. TS-GCN: A novel tumor segmentation method integrating transformer and GCN[J]. Mathematical Biosciences and Engineering, 2023, 20(10): 18173-18190. doi: 10.3934/mbe.2023807

    Related Papers:

    [1] Luis Caffarelli, Antoine Mellet . Random homogenization of fractional obstacle problems. Networks and Heterogeneous Media, 2008, 3(3): 523-554. doi: 10.3934/nhm.2008.3.523
    [2] Joachim von Below, José A. Lubary . Isospectral infinite graphs and networks and infinite eigenvalue multiplicities. Networks and Heterogeneous Media, 2009, 4(3): 453-468. doi: 10.3934/nhm.2009.4.453
    [3] Delio Mugnolo . Gaussian estimates for a heat equation on a network. Networks and Heterogeneous Media, 2007, 2(1): 55-79. doi: 10.3934/nhm.2007.2.55
    [4] Caihong Gu, Yanbin Tang . Global solution to the Cauchy problem of fractional drift diffusion system with power-law nonlinearity. Networks and Heterogeneous Media, 2023, 18(1): 109-139. doi: 10.3934/nhm.2023005
    [5] Giuseppe Maria Coclite, Lorenzo di Ruvo . H1 solutions for a modified Korteweg-de Vries-Burgers type equation. Networks and Heterogeneous Media, 2024, 19(2): 724-739. doi: 10.3934/nhm.2024032
    [6] Kota Kumazaki, Adrian Muntean . Local weak solvability of a moving boundary problem describing swelling along a halfline. Networks and Heterogeneous Media, 2019, 14(3): 445-469. doi: 10.3934/nhm.2019018
    [7] Hantaek Bae, Rafael Granero-Belinchón, Omar Lazar . On the local and global existence of solutions to 1d transport equations with nonlocal velocity. Networks and Heterogeneous Media, 2019, 14(3): 471-487. doi: 10.3934/nhm.2019019
    [8] Debora Amadori, Stefania Ferrari, Luca Formaggia . Derivation and analysis of a fluid-dynamical model in thin and long elastic vessels. Networks and Heterogeneous Media, 2007, 2(1): 99-125. doi: 10.3934/nhm.2007.2.99
    [9] Markus Musch, Ulrik Skre Fjordholm, Nils Henrik Risebro . Well-posedness theory for nonlinear scalar conservation laws on networks. Networks and Heterogeneous Media, 2022, 17(1): 101-128. doi: 10.3934/nhm.2021025
    [10] Steinar Evje, Kenneth H. Karlsen . Hyperbolic-elliptic models for well-reservoir flow. Networks and Heterogeneous Media, 2006, 1(4): 639-673. doi: 10.3934/nhm.2006.1.639
  • As one of the critical branches of medical image processing, the task of segmentation of breast cancer tumors is of great importance for planning surgical interventions, radiotherapy and chemotherapy. Breast cancer tumor segmentation faces several challenges, including the inherent complexity and heterogeneity of breast tissue, the presence of various imaging artifacts and noise in medical images, low contrast between the tumor region and healthy tissue, and inconsistent size of the tumor region. Furthermore, the existing segmentation methods may not fully capture the rich spatial and contextual information in small-sized regions in breast images, leading to suboptimal performance. In this paper, we propose a novel breast tumor segmentation method, called the transformer and graph convolutional neural (TS-GCN) network, for medical imaging analysis. Specifically, we designed a feature aggregation network to fuse the features extracted from the transformer, GCN and convolutional neural network (CNN) networks. The CNN extract network is designed for the image's local deep feature, and the transformer and GCN networks can better capture the spatial and context dependencies among pixels in images. By leveraging the strengths of three feature extraction networks, our method achieved superior segmentation performance on the BUSI dataset and dataset B. The TS-GCN showed the best performance on several indexes, with Acc of 0.9373, Dice of 0.9058, IoU of 0.7634, F1 score of 0.9338, and AUC of 0.9692, which outperforms other state-of-the-art methods. The research of this segmentation method provides a promising future for medical image analysis and diagnosis of other diseases.



    The 3D incompressible resistive Hall-Magnetohydrodynamics system (Hall-MHD in short) is the following system of PDEs for (u,p,B):

    ut+uuBB+pμΔu=0, (1a)
    Bt+uBBu+curl((curlB)×B)νΔB=0, (1b)
    divu=0,divB=0, (1c)

    where u=(u1,u2,u3) is the plasma velocity field, p is the pressure, and B=(B1,B2,B3) is the magnetic field. μ and ν are the viscosity and the resistivity constants, respectively. The Hall-MHD is important in describing many physical phenomena [2,17,19,23,26,27,33]. In particular, the Hall MHD explains magnetic reconnection on the Sun which is very important role in acceleration plasma by converting magnetic energy into bulk kinetic energy.

    The Hall-MHD recently has been studied intensively. The Hall-MHD can be derived from either two fluids model or kinetic models in a mathematically rigorous way [1]. Global weak solution, local classical solution, global solution for small data, and decay rates are established in [4,5,6]. There have been many follow-up results of these papers; see [7,8,12,13,14,15,16,18,29,30,31,32,34,35] and references therein.

    We note that the Hall term curl((curlB)×B) is dominant in mathematical analysis of (1) and so we only consider the Hall equations ((u,p)=0 in (1)). Also motivated by [7], we consider the Hall equation with fractional Laplacian:

    Bt+curl((curlB)×B)+ΛβB=0,divB=0, (2)

    where we take ν=1 for simplicity. (2) is locally well-posed [7] when β>1. But, we do not know whether (2) is locally well-posed when β=1:

    Bt+curl((curlB)×B)+ΛB=0,divB=0. (3)

    However, we can show the existence of solutions globally in time if initial data is sufficiently small.

    Theorem 1.1. Let B0Hk with k>52 and divB0=0. There exists a constant ϵ0>0 such that if B0Hkϵ0, there exists a unique global-in-time solution of (3) satisfying

    B(t)2Hk+(1Cϵ0)t0Λ12B(s)2HkdsB02Hkforallt>0.

    Moreover, B decays in time

    ΛlB(t)L2C0(1+t)l,0<lk, (4)

    where C0 depends on B0Hk which is expressed in (27) explicitly.

    Remark 1. The decay rate (4) is consistent with the decay rates of the linear part of (3).

    Remark 2. After this work was completed, the referee pointed out that the same result is proved in [37,Theorem 1.1]. Compared to the proof in [37] where they use the Littlewood-Paley decomposition, we use the standard energy energy estimates and classical commutator estimates.

    As one of a minimal modification of (3) to show the existence of unique local in time solutions, we now take a logarithmic correction of (3):

    Bt+curl((curlB)×B)+ln(2+Λ)ΛB=0, (5)

    where the Fourier symbol of ln(2+Λ)Λ is ln(2+|ξ|)|ξ|.

    Theorem 1.2. Let B0Hk with k>52 and divB0=0. There exists T=T(B0Hk)>0 such that there exists a unique local-in-time solution of (5) satisfying

    B(t)Hkln(1eB0HkCt),0<t<T=exp(B0Hk)C. (6)

    In this paper, we also deal with 2D models closely related to the 212 dimensional (3). If we take B of the form

    B(t,x,y)=(ψy(t,x,y),ψx(t,x,y),Z(t,x,y)), (7)

    we can rewrite (3) as

    ψt+Λψ=[ψ,Z], (8a)
    Zt+ΛZ=[Δψ,ψ], (8b)

    where [f,g]=fg=fxgyfygx. (7) is used to show a finite-time collapse to a current sheet [3,20,21,24] and is used in [10] to study regularity of stationary weak solutions.

    Although (8) is defined in 2D and has nice cancellation properties (18), the local well-posedness seems unreachable. But, suppose that we redistribute the power of the fractional Laplacians in (8) in such a way that (8b) has the full Laplacian and (8a) is inviscid:

    ψt=[ψ,Z],ZtΔZ=[Δψ,ψ]. (9)

    (9) has no direct link to (2), but we may interpret (9) as the 212 dimensional model of the Hall equations where only B3 has the full Laplacian in (2). In this case, we can show that (9) is locally well-posed. Let

    E(t)=ψ(t)2H4+Z(t)2H3,E0=ψ02H4+Z02H3. (10)

    Theorem 1.3. There exists T=T(E0)>0 such that there exists a unique solution of (9) satisfying

    E(t)E011CtE0forall 0<tT<1CE0.

    Moreover, we have the following blow-up criterion:

    E(t)+t0Z(s)2H2ds<t0(2Z(s)L+2ψ(s)2L)ds<.

    Since there is no dissipative effect in the equation of ψ in (9), we only have the local in tim result in Theorem 1.3. Among the possible conditions for the global existence, we find that adding a damping term to the equation of ψ works. More precisely, we deal with the following

    ψt+ψ=[ψ,Z],ZtΔZ=[Δψ,ψ]. (11)

    In this case, we can show the existence of global in time solutions with small initial data having regularity one higher than the regularity in Theorem 1.3. Moreover, we can find decay rates of ψ by using the structure of equation of ψ which is a damped transport equation, and this is also the reason why the same method cannot be applied to Z. Let

    F(t)=ψ(t)2H5+Z(t)2H4,F0=ψ02H5+Z02H4,N1(t)=ψ(t)2H4+Z(t)2H4.

    Theorem 1.4. There exists a constant ϵ0>0 such that if F0ϵ0, there exists a unique global-in-time solution of (11) satisfying

    F(t)+(1Cϵ0)t0N1(s)dsF0forallt>0.

    Moreover, ψ decays exponentially in time

    ψ(t)L2ψ0L2et,Λkψ(t)L2Fk180ψ05k4L2e(5k)(1Cϵ0)4t

    with 1k<5.

    As another way to redistribute the derivatives in (8), we also deal with

    ψtΔψ=[ψ,Z],Zt=[Δψ,ψ]. (12)

    Let E(t) and E0 be defined as before (10).

    Theorem 1.5. There exists T>0, which is depending on E0, such that there exists a unique solution of (12) satisfying

    E(t)E01CtE0forall 0<tT<1CE0.

    Moreover, we have the following blow-up criterion

    E(t)+t0ψ2H4ds<t02ψ2Lds.

    We now add a damping term to the equation of Z in (12):

    ψtΔψ=[ψ,Z],Zt+Z=[Δψ,ψ]. (13)

    In this case, we can use the same regularity used in Theorem 1.5 because the dissipative effect in ψ helps to control Δψ in the equation of Z. Let N2(t)=ψ(t)2H5+Z(t)2H3.

    Theorem 1.6. There exists a constant ϵ0>0 such that if E0ϵ0, there exists a unique global-in-time solution of (13) satisfying

    E(t)+(1Cϵ0)t0N2(s)dsE0forallt>0.

    Remark 3. Compared to Theorem 1.3, we only need one term in the blow-up criterion in Theorem 1.5 which is due to the dissipative effect in the equation of ψ. Compared to Theorem 1.4, the proof of Theorem 1.6 is simpler, but we are not able to derive decay rates of ψ and Z.

    All constants will be denoted by C and we follow the convention that such constants can vary from expression to expression and even between two occurrences within the same expression. And repeated indices are summed over.

    The fractional Laplacian Λβ=(Δ)β has the Fourier transform representation

    ^Λβf(ξ)=|ξ|βˆf(ξ).

    For s>0, Hs is a energy space equipped with

    fHs=fL2+f˙Hs,f˙Hs=ΛsfL2.

    In the energy spaces, we have the following interpolations: for s0<s<s1

    f˙Hsfθ˙Hs0f1θ˙Hs1,s=θs0+(1θ)s1. (14)

    We begin with two inequalities in 3D:

    fLCfHs,s>32, (15a)
    fLpCf˙Hs,1p=12s3. (15b)

    We also provide the following inequalities in 2D

    fL4Cf12L2f12L2,fLCf12L2Δf12L2

    which will be used repeatedly in the proof of Theorem 1.3, Theorem 1.4, Theorem 1.5, and Theorem 1.6. We also recall

    2fL2=ΔfL2

    which holds in any dimension.

    We finally provide the Kato-Ponce commutator cstimate [22]

    [Λk,f]gL2=Λk(fg)fΛkgL2CfLΛk1gL2+CgLΛkfL2 (16)

    and the fractional Leibniz rule [11]: for 1p< and pi,qi1,

    Λs(fg)LpCΛsfLp1gLq1+CfLp2ΛsgLq2,1p=1p1+1q1=1p2+1q2. (17)

    We recall the commutator [f,g]=fg=fxgyfygx. Then, the commutator has the following properties:

    Δ[f,g]=[Δf,g]+[f,Δg]+2[fx,gx]+2[fy,gy], (18a)
    f[f,g]=0, (18b)
    f[g,h]=g[h,f]. (18c)

    We recall (3):

    Bt+curl((curlB)×B)+ΛB=0. (19)

    We first approximate (19) by putting ϵΔB to the right-hand side of (19):

    Bt+curl((curlB)×B)+ΛB=ϵΔB. (20)

    We then mollify (20) as follows

    tB(ϵ)+curl(Jϵ(curlJϵB(ϵ))×JϵB(ϵ))+ΛJ2ϵB(ϵ)=ϵJ2ϵΔB(ϵ),B(ϵ)0=JϵB0, (21)

    where Jϵ is the standard mollifier described in [25,Chapter 3.2]. Then, as proved in [4,Proposition 3.1], there exists a unique global-in-time solution {B(ϵ)} of (21). Since the bounds in Section 3.1.2 are independent of ϵ>0, we can pass to the limit in a subsequence and show the existence of smooth solutions globally in time when B0Hk, k>52, is sufficiently small as in [37,Section 3.2].

    We begin with the L2 bound:

    12ddtB2L2+Λ12B2L2=0. (22)

    We now take Λk to (19) and take the inner product of the resulting equation with ΛkB. Then,

    12ddtΛkB2L2+Λ12+kB2L2=Λkcurl((curlB)×B)ΛkB=([Λ12+k,B]×curlB)Λk12curlB[Λ12+k,B]×curlBL2Λ12+kBL2.

    By (16) and (15a) with k>52,

    [Λ12+k,B]×curlBL2CBLΛk12curlBL2CBHkΛ12+kB2L2. (23)

    So, we obtain

    ddtΛkB2L2+Λ12+kB2L2CBHkΛ12+kB2L2. (24)

    By (22) and (24),

    ddtB2Hk+Λ12B2HkCBHkΛ12+kB2L2.

    If B0Hk=ϵ0 is sufficiently small, we can derive a uniform bound

    B(t)2Hk+(1Cϵ0)t0Λ12B(s)2HkdsB02Hkforallt>0. (25)

    Let B1 and B2 be two solutions of (19). Then, B=B1B2 satisfies

    Bt+ΛB+curl((curlB1)×B)curl((curlB)×B2)=0 (26)

    with B0=0. We take the inner product of (26) with B. By (17) with k>52,

    12ddtB2L2+Λ12B2L2=(curl((curlB1)×B))B=Λ12(((curlB1)×B))Λ12curlBCB1LΛ12B2L2+CΛ12B1L6BL3Λ12BL2CB1LΛ12B2L2+CΛ52B1L2Λ12B2L2CB1HkΛ12B2L2,

    where we use (15b) to control L6 and L3 terms. If Cϵ0<1, (25) implies B=0 in L2 which gives the uniqueness of a solution.

    By (14), it is enough to derive the decay rate with k=l to show (4). Since

    ΛkB2k+1kL2B1kL2Λ12+kB2L2B01kL2Λ12+kB2L2

    by (14) and (22), we create the following ODE from (24)

    ddtΛkB2L2+1Cϵ0B01kL2ΛkB2k+1kL20.

    By solving this ODE, we find the following decay rate

    ΛkB(t)L2((2k)kB0L2ΛkB0L2)(2kB01kL2+(1Cϵ0)ΛkB01kL2t)k. (27)

    We recall (5):

    Bt+curl((curlB)×B)+ln(2+Λ)ΛB=0,

    The the uniqueness part of Theorem 1.2 is the same as that of Theorem 1.1 and we only derive a priori bounds. Let

    ln(2+Λ)Λsf2L2=(ln(2+|ξ|))|ξ|2s|ˆf(ξ)|2dξ.

    We begin with the L2 bound:

    12ddtB2L2+ln(2+Λ)Λ12B2L2=0. (28)

    Following the computations in the proof of Theorem 1.1, we also have

    ddtΛkB2L2+ln(2+Λ)Λ12+kB2L2CBHkΛ12+kB2L2. (29)

    For each NN, we have

    Λ12+kB2L2=|ξ|2N|ξ|2k+1|ˆB(ξ)|2dξ+|ξ|2N|ξ|2k+1|ˆB(ξ)|2dξ2N|ξ|2N|ξ|2k|ˆB(ξ)|2dξ+1ln(2+2N)|ξ|2Nln(2+|ξ|)|ξ|2k+1|ˆB(ξ)|2dξ2NΛkB2L2+1ln(2+2N)ln(2+Λ)Λ12+kB2L2.

    So, (29) is replaced by

    ddtΛkB2L2+ln(2+Λ)Λ12+kB2L2C2NΛkB2L2BHk+CBHkln(2+2N)ln(2+Λ)Λ12+kB2L2.

    We now choose N>0 such that

    12ln(2+2N)<CBHk<ln(2+2N)

    and so NBHk. Then, (29) is reduced to

    ddtΛkB2L2Cexp(BHk)BHkΛkBL2. (30)

    By (28) and (30), we obtain

    ddtB2HkCexp(BHk)B2Hk

    and so we have

    ddtBHkCexp(BHk)BHkCexp(BHk).

    By solving this ODE, we can derive (6).

    We recall (9):

    ψt=[ψ,Z], (31a)
    ZtΔZ=[Δψ,ψ]. (31b)

    We first approximate (31a) by putting ϵΔψ to the right-hand side and mollify the resulting equations as (21). Then, we have

    tψ(ϵ)=Jϵ[Jϵψ(ϵ),JϵZ(ϵ)]+ϵJ2ϵΔψ(ϵ),tZ(ϵ)ΔJ2ϵZ(ϵ)=Jϵ[ΔJϵψ(ϵ),Jϵψ(ϵ)] (32)

    with ψ(ϵ)0=Jϵψ0 and Z(ϵ)0=JϵZ0. Since (32) is defined in R2, the proof of the existence of a unique global-in-time solution of (32) is relatively easier than the one to (21). Moreover, the bounds in Section 4.1.2 are independent of ϵ>0 and so we can pass to the limit in a subsequence and show the existence of smooth solutions locally in time when ψ0H4 and Z0H3.

    We first note that

    12ddtψ2L2=ψ[ψ,Z]=0. (33)

    We next multiply (31a) by Δψ, (31b) by Z, and integrate over R2. By (18c),

    12ddt(ψ2L2+Z2L2)+Z2L2=(Δψ[ψ,Z]+Z[Δψ,ψ])=0. (34)

    We also multiply (31a) by Δ4ψ, (31b) by Δ3Z and integrate over R2. Then,

    12ddt(Δ2ψ2L2+ΔZ2L2)+Δ2Z2L2=Δ4ψ[ψ,Z]Δ3Z[Δψ,ψ]=R. (35)

    We now compute the right-hand side of (35). By (18a), (18b), and (18c),

    R=2Δ2ψ[Δψ,ΔZ]+4Δ2ψ[ψx,ΔZx]+4Δ2ψ[ψy,ΔZy]+4Δ2ψ[Δψx,Zx]+4Δ2ψ[Δψy,Zy]+4Δ2ψ[ψxx,Zxx]+8Δ2ψ[ψxy,Zxy]+4Δ2ψ[ψyy,Zyy]2Δ2Z[Δψx,ψx]2Δ2Z[Δψy,ψy]. (36)

    So, we find that the number of derivatives acting on (ψ,ψ,Z) are (4,4,2), (3,4,3), and (4,2,4) up to multiplicative constants. Hence,

    ddt(Δ2ψ2L2+ΔZ2L2)+Δ2Z2L2C|4ψ||4ψ||2Z|+C|3ψ||4ψ||3Z|+C|4ψ||2ψ||4Z|CΔ2ψ2L22ZL+C3ψL4Δ2ψL23ZL4+CΔ2ψL22ψLΔ2ZL2CΔ2ψ2L22ZL+CΔ2ψ32L2Δψ12L2Δ2ZL2+CΔ2ψL22ψLΔ2ZL2CE2+14Δ2Z2L2+δ2Z2LCE2+12Δ2Z2L2+14Z2L2,

    where we use

    2Z2LCΔZL2Δ2ZL2CZ23L2Δ2Z43L2CZ2L2+CΔ2Z2L2

    with δ satisfying 4Cδ=1. So, we have

    ddt(Δ2ψ2L2+ΔZ2L2)+Δ2Z2L2CE2+12Z2L2. (37)

    By (33), (34), and (37), we derive ECE2 from which we deduce

    E(t)E01CtE0forall 0<tT<1CE0. (38)

    Let (ψ1,Z1) and (ψ2,Z2) be two solutions of (31) and let ψ=ψ1ψ2 and Z=Z1Z2. Then, (ψ,Z) satisfies the following equations:

    ψt=[ψ,Z1]+[ψ2,Z],ZtΔZ=[Δψ,ψ1]+[Δψ2,ψ]

    with ψ(0,x)=Z(0,x)=0. For these equations, we have

    12ddt(ψ2L2+Z2L2)+Z2L2=Δψ[ψ,Z1]Δψ[ψ2,Z]+Z[Δψ,ψ1]+Z[Δψ2,ψ]=(I)+(II)+(III)+(IV).

    The first term is bounded using the definition of [f,g] and divZ1=0:

    (I)=(Z1ψ)Δψ=(lZ1ψ)lψC2Z1Lψ2L2.

    We next bound (II)+(III) as

    (II)+(III)=Z[Δψ,ψ]C2ψLψL2ZL2C(2ψ12L+2ψ22L)ψ2L2+14Z2L2.

    The last term is bounded as

    (IV)C2ψ2LψL2ZL2C2ψ22Lψ2L2+14Z2L2.

    So, we have

    ddt(ψ2L2+Z2L2)C(2Z1L+2ψ12L+2ψ22L)(ψ2L2+Z2L2). (39)

    By (38), 2ψ12L+2ψ22L is integrable in time. Integrating (34) and (35) in time, we have

    t0(Z(s)2L2+Δ2Z(s)2L2)ds<for0<tT2

    which gives the integrability of the first term in the parentheses on the right-hand side of (39). By repeating the same argument one more time, we have the uniqueness up to T.

    Let

    B(s)=2Z(s)L+2ψ(s)2L.

    We first deal with

    12ddt(Δψ2L2+Z2L2)+ΔZ2L2=Δ2ψ[ψ,Z]ΔZ[Δψ,ψ]=2Δψ[ψx,Zx]+2Δψ[ψy,Zy]C2ZLΔψ2L2

    and so we have

    ddt(Δψ2L2+Z2L2)+ΔZ2L2C2ZLΔψ2L2.

    This implies

    Δψ(t)2L+Z(t)2L2+t0ΔZ(s)2L2ds<t02Z(s)Lds<. (40)

    We also deal with

    12ddt(Δψ2L2+ΔZ2L2)+ΔZ2L2=Δ3ψ[ψ,Z]+Δ2Z[Δψ,ψ]=Δ2ψ[Δψ,Z]2Δ2ψ([ψx,Zx]+[ψy,Zy])2Δψ([ψx,ΔZx]+[ψy,ΔZy])=(I)+(II)+(III).

    As in Section 4.1.3,

    (I)=(ZΔψ)ΔψC2ZLΔψ2L2. (41)

    We next estimate (II)+(III):

    (II)+(III)=4Δψ([Δψy,Zy]+[ψy,ΔZy]+[ψxy,Zxy]+[ψyy,Zyy])C|2Z||3ψ|2+C|2ψ||3ψ||3Z|C2ZLΔψ2L2+C2ψ2LΔψ2L2+12ΔZ2L2. (42)

    By (41) and (42), we have

    ddt(Δψ2L2+ΔZ2L2)+ΔZ2L2C(2ZL+2ψ2L)Δψ2L2

    which implies

    Δψ(t)2L2+ΔZ(t)2L2+t0ΔZ(s)2L2ds<t0B(s)ds<. (43)

    We finally deal with

    12ddt(Δ2ψ2L2+ΔZ2L2)+Δ2Z2L2=Δ4ψ[ψ,Z]Δ3Z[Δψ,ψ]=R

    with the same R in (36). So, we have

    12ddt(Δ2ψ2L2+ΔZ2L2)+Δ2Z2L2C2ZLΔ2ψ2L2+C2ψLΔ2ZL2Δ2ψL2+CΔZL4ΔψL4Δ2ψL2C(2ZL+2ψ2L+ΔZ32L2Δψ32L2)Δ2ψ2L2+12Δ2Z2L2

    which gives

    ddt(Δ2ψ2L2+ΔZ2L2)+Δ2Z2L2C(B(s)+ΔZ32L2Δψ32L2)Δ2ψ2L2. (44)

    By (40) and (43), (44) implies

    Δ2ψ(t)2L2+ΔZ(t)2L2+t0Δ2Z(s)2L2ds<t0B(s)ds<.

    We recall (11):

    ψt+ψ=[ψ,Z],ZtΔZ=[Δψ,ψ]

    Since the uniqueness is already proved in Section 4.1.3 even without the damping term, we only focus on the a priori bounds and the decay rates.

    We first have

    12ddtψ2L2+ψ2L2=0,12ddt(ψ2L2+Z2L2)+ψ2L2+Z2L2=0. (45)

    We now consider the highest order part:

    12ddt(Δ2ψ2L2+Δ2Z2L2)+Δ2ψ2L2+Δ2Z2L2=Δ5ψ[ψ,Z]+Δ4Z[Δψ,ψ].

    We compute the right-hand side of this. By (18a), (18b), and (18c),

    Δ5ψ[ψ,Z]+Δ4Z[Δψ,ψ]=2Δ3Z[Δψx,ψx]+2Δ3Z[Δψy,ψy]+2Δ2Z[Δ2ψx,ψx]+2Δ2Z[Δ2ψy,ψy]+2ΔZ[Δ2ψx,Δψx]+2ΔZ[Δ2ψy,Δψy]Δ3ψ[Δψ,ΔZ]2Δ3ψ[Δψx,Zx]2Δ3ψ[Δψy,Zy]2Δ3ψ[ψx,ΔZx]2Δ3ψ[ψy,ΔZy]2Δ4ψ[ψx,Zx]2Δ4ψ[ψy,Zy]Δ3ψ[Δ2ψ,Z]. (46)

    We now count the number of derivatives hitting on (Z,ψ,ψ) using the integration by parts and (18b) and (18c) up to multiplicative constants. Except for the last integral, we have

    (6,2,4)(5,2,5), (5,3,4)(4,2,6)(5,5,2), (4,3,5)(2,2,8)(3,2,7)(4,2,6), (3,3,6)(5,5,2), (4,3,5)(2,4,6)(2,5,5), (3,4,5).

    The last integral is

    (ZΔ2ψ)Δ3ψ=(lZΔ2ψ)lΔ2ψ

    and so this gives (2,5,5). So, the combinations of the numbers of derivatives taken on (Z,ψ,ψ) are

    (2,5,5), (3,4,5), (4,3,5), (5,2,5), (5,3,4).

    The first and the fourth cases are bounded by

    C2ZLΔ2ψ2L2C2Z2LΔ2ψ2L2+16Δ2ψ2L2,C2ψLΔ2Z2L2C2ψLΔ2Z2L2+14Δ2Z2L2.

    The second case is bounded by

    C3ZL44ψL4Δ2ψL2CΔZ12L2Δ2Z12L2Δ2ψ12L2Δ2ψ32L2CΔZ2L2Δ2ψ2L2Δ2Z2L2+16Δ2ψ2L2.

    The third case is bounded by

    C4ZL43ψL4Δ2ψL2CΔ2Z12L2Δ2Z12L2Δψ12L2Δ2ψ32L2CΔψ2L2Δ2Z2L2Δ2Z2L2+16Δ2ψ2L2.

    The last one is bounded by

    C3ψL44ψL4Δ2ZL2CΔψ12L2Δ2ψL2Δ2ψ12L2Δ2ZL2CΔψL2Δ2ψL2Δ2ZL2CΔψ2L2Δ2ψ2L2+14Δ2Z2L2.

    So, we obtain

    ddt(Δ2ψ2L2+Δ2Z2L2)+Δ2ψ2L2+Δ2Z2L2C2Z2LΔ2ψ2L2+C2ψ2LΔ2Z2L2+CΔψ2L2Δ2ψ2L2+CΔZ2L2Δ2ψ2L2Δ2Z2L2+CΔψ2L2Δ2Z2L2Δ2Z2L2 (47)

    By (45) and (47),

    F(t)+N1(t)C(F(t)+F2(t))N1(t).

    So, if F0=ϵ0 is sufficiently small, we obtain

    F(t)+(1Cϵ0)t0N1(s)dsF0forallt>0.

    From (45), ψ(t)L2ψ0L2et. Since

    12ddtψ2L2+ψ2L2=Δψ[ψ,Z]=(Zψ)Δψ=(lZψ)lψ2ZLψ2L2Cϵ0ψ2L2,

    we have

    ψ(t)L2ψ0L2e(1Cϵ0)t.

    By using (14), we also obtain

    Λkψ(t)L2Fk180ψ05k4L2e(5k)(1Cϵ0)4t,1k<5.

    We recall (12):

    ψtΔψ=[ψ,Z],Zt=[Δψ,ψ].

    By applying the same approximation and mollification methods in Section 4.1.1, we can show the existence of smooth solutions locally in time when ψ0H4 and Z0H3.

    We first have

    12ddtψ2L2+ψ2L2=0,12ddt(ψ2L2+Z2L2)+Δψ2L2=0. (48)

    We next deal with

    12ddt(Δ2ψ2L2+ΔZ2L2)+Δ2ψ2L2=Δ4ψ[ψ,Z]Δ3Z[Δψ,ψ]=R

    with the same R in (36). In this case, we choose the the number of derivatives acting on (ψ,ψ,Z) different from Section 4.1.2, which are given by (3,5,2), (2,5,3), and (4,4,2) after several integration by parts. Hence, we have

    ddt(Δ2ψ2L2+ΔZ2L2)+Δ2ψ2L2C2ZL2Δ2ψ2L4+CΔZL22ψLΔ2ψL2+CΔZL43ψL4Δ2ψL2CE21+12Δ2ψ2L2

    and so we have the following bound

    ddt(Δ2ψ2L2+ΔZ2L2)+Δ2ψ2L2CE2. (49)

    By (48) and (49), we derive ECE2 from which we deduce

    E(t)E01CtE0forall 0<tT<1CE0. (50)

    Let (ψ1,Z1) and (ψ2,Z2) be two solutions and let ψ=ψ1ψ2 and Z=Z1Z2. As in Section 4.1.3

    12ddt(ψ2L2+Z2L2)+Δψ2L2=Δψ[ψ,Z1]Δψ[ψ2,Z]+Z[Δψ,ψ1]+Z[Δψ2,ψ]=(I)+(II)+(III)+(IV).

    The first term three terms are bounded as

    (I)Z1LψL2ΔψL2CZ12Lψ2L2+13Δψ2L2,(II)+(III)=Z[Δψ,ψ]CZLψL2ΔψL2C(Z12L+Z22L)ψ2L2+13Δψ2L2

    The last term is bounded as

    (IV)C3ψ2L4ψL4ZL2C3ψ2L4ψ12L2Δψ12L2ZL2C3ψ243L4ψ23L2Z43L2+13Δψ2L2C3ψ24L4ψ2L2+CZ2L2+13Δψ2L2CΔψ22L2Δ2ψ22L2ψ2L2+CZ2L2+13Δψ2L2.

    So, we have

    ddt(ψ2L2+Z2L2)C(Z12L+Z22L+Δψ22L2Δ2ψ22L2)(ψ2L2+Z2L2).

    By (50), the terms in the parentheses are integrable up to T2. By repeating the same argument one more time, we have the uniqueness up to T.

    To derive the blow-up criterion, we first bound

    12ddt(Δψ2L2+Z2L2)+Δψ2L2=Δ2ψ[ψ,Z]ΔZ[Δψ,ψ]=2Δψ[ψx,Zx]+2Δψ[ψy,Zy]C2ψLZL2ΔψL2C2ψ2LZ2L2+12Δψ2L2

    and so we have

    ddt(Δψ2L2+Z2L2)+Δψ2L2C2ψ2LZ2L2.

    This implies

    Δψ(t)2L+Z(t)2L2+t0Δψ(s)2L2ds<t02ψ(s)2Lds< (51)

    We also take

    12ddt(Δψ2L2+ΔZ2L2)+Δ2ψ2L2=Δ3ψ[ψ,Z]+Δ2Z[Δψ,ψ]=Δ2ψ[Δψ,Z]2Δ2ψ([ψx,Zx]+[ψy,Zy])2Δψ([ψx,ΔZx]+[ψy,ΔZy])=(I)+(II)+(III).

    By using the computation in (41),

    (I)=(ZΔψ)ΔψC2ZL23ψ2L4C2Z2L2Δψ2L2+16Δ2ψ2L2.

    We next estimate (II)+(III) using (42):

    (II)+(III)C|2Z||3ψ|2+C|2ψ||4ψ||2Z|CΔZ2L2Δψ2L2+C2ψ2LΔZ2L2+13Δ2ψ2L2

    So, we have

    ddt(Δψ2L2+ΔZ2L2)+Δ2ψ2L2C(Δψ2L2+2ψ2L)ΔZ2L2. (52)

    By (51), (52) implies

    Δψ(t)2L2+ΔZ(t)2L2+Δ2ψ(s)2L2ds<t02ψ(s)2Lds<. (53)

    We finally deal with

    12ddt(Δ2ψ2L2+ΔZ2L2)+Δ2ψ2L2=Δ4ψ[ψ,Z]Δ3Z[Δψ,ψ]

    where we count the number of derivatives acting on (ψ,ψ,Z) in (46) as (3,5,2), (2,5,3), and (4,4,2). Then, we obtain

    12ddt(Δ2ψ2L2+ΔZ2L2)+Δ2ψ2L2CΔZ2L2Δ2ψ2L2+C2ψ2LΔZ2L2+CΔZ2L43ψ2L4+12Δ2ψ2L2CΔZ2L2Δ2ψ2L2+C2ψ2LΔZ2L2+CΔZ2L2ΔZ2L2+CΔψ2L2Δ2ψ2L2+12Δ2ψ2L2

    and so we have

    ddt(Δ2ψ2L2+ΔZ2L2)+Δ2ψ2L2C(2ψ2L+ΔZ2L2)ΔZ2L2+C(Δψ2L2+ΔZ2L2)Δ2ψ2L2 (54)

    By (51) and (53), (54) implies

    Δ2ψ(t)2L2+ΔZ(t)2L2+t0Δ2ψ(s)2L2ds<t02ψ(s)2Lds<.

    We recall (13):

    ψtΔψ=[ψ,Z],Zt+Z=[Δψ,ψ].

    Since the uniqueness is already proved in Section 5.1.2 even without the damping term, we only focus on the a priori bounds.

    We first have

    12ddtψ2L2+ψ2L2=0,12ddt(ψ2L2+Z2L2)+Δψ2L2+Z2L2=0. (55)

    We also have

    12ddt(Δ2ψ2L2+ΔZ2L2)+Δ2ψ2L2+ΔZ2L2=Δ4ψ[ψ,Z]Δ3Z[Δψ,ψ]=R

    with the same R in (36). In this case, we also choose the the number of derivatives acting on (ψ,ψ,Z) as (3,5,2), (2,5,3), and (4,4,2). Hence,

    ddt(Δ2ψ2L2+ΔZ2L2)+Δ2ψ2L2+ΔZ2L2C2ZL2Δ2ψ2L4+CΔZL22ψLΔ2ψL2+CΔZL43ψL4Δ2ψL2CZ12L2ΔZ12L2Δψ12L2Δ2ψ32L2+CΔZL22ψLΔ2ψL2+CΔZ12L2ΔZ12L2Δψ12L2Δ2ψ32L2C(Z2L2Δψ2L2+ΔZ2L2Δψ2L2+2ψ2L)ΔZ2L2+12Δ2ψL2.

    So, we obtain

    ddt(Δ2ψ2L2+ΔZ2L2)+Δ2ψ2L2+ΔZ2L2C(Z2L2Δψ2L2+ΔZ2L2Δψ2L2+2ψ2L)ΔZ2L2 (56)

    By (55) and (56),

    E(t)+N2(t)C(E(t)+E2(t))N2(t).

    So, if E0=ϵ0 is sufficiently small, we obtain

    E(t)+(1Cϵ0)t0N2(s)dsE0forallt>0.

    H.B. was supported by NRF-2018R1D1A1B07049015. H. B. acknowledges the Referee for his/her valuable comments that highly improve the manuscript.



    [1] M. H. Yap, G. Pons, J. Marti, S. Ganau, M. Sentis, R. Zwiggelaar, et al., Automated breast ultrasound lesions detection using convolutional neural networks, IEEE J. Biomed. Health Inf., 22 (2018), 1218–1226. https://doi.org/10.1109/JBHI.2017.2731873 doi: 10.1109/JBHI.2017.2731873
    [2] J. Gao, Q. Jiang, B. Zhou, D. Chen, Convolutional neural networks for computer-aided detection or diagnosis in medical image analysis: An overview, Math. Biosci. Eng., 16 (2019), 6536–6561. https://doi.org/10.3934/mbe.2019326 doi: 10.3934/mbe.2019326
    [3] C. Xu, Y. Qi, Y. Wang, M. Lou, J. Pi, Y. Ma, ARF-Net: An adaptive receptive gield network for breast mass segmentation in whole mammograms and ultrasound images, Biomed. Signal Process. Control, 71 (2022), 103178. https://doi.org/10.1016/j.bspc.2021.103178 doi: 10.1016/j.bspc.2021.103178
    [4] Y. Wang, N. Wang, M. Xu, J. Yu, C. Qin, X. Luo, et al., Deeply-supervised networks with threshold loss for cancer detection in automated breast ultrasound, IEEE Trans. Med. Imaging, 39 (2019), 866–876. https://doi.org/10.1109/TMI.2019.2936500 doi: 10.1109/TMI.2019.2936500
    [5] S. Jiang, J. Li, Z. Hua, Transformer with progressive sampling for medical cellular image segmentation, Math. Biosci. Eng., 19 (2022), 12104–12126. https://doi.org/10.3934/mbe.2022563 doi: 10.3934/mbe.2022563
    [6] A. Iqbal, M. Sharif, MDA-Net: Multiscale dual attention-based network for breast lesion segmentation using ultrasound images, J. King Saud Univ. Comput. Inf. Sci., 34 (2022), 7283–7299. http://dx.doi.org/10.1016/j.jksuci.2021.10.002 doi: 10.1016/j.jksuci.2021.10.002
    [7] R. Bi, C. Ji, Z. Yang, M. Qiao, P. Lv, H. Wang, Residual-based attention-Unet combing DAC and RMP modules for automatic liver tumor segmentation in CT, Math. Biosci. Eng., 19 (2022), 4703–4718. https://doi.org/10.3934/mbe.2022219 doi: 10.3934/mbe.2022219
    [8] B. Lei, S. Huang, R. Li, C. Bian, H. Li, Y. H. Chou, et al., Segmentation of breast anatomy for automated whole breast ultrasound images with boundary regularized convolutional encoder-decoder network, Neurocomputing, 321 (2018), 178–186. https://doi.org/10.1016/j.neucom.2018.09.043 doi: 10.1016/j.neucom.2018.09.043
    [9] Y. Ouyang, Z. Zhou, W. Wu, J. Tian, F. Xu, S. Wu, et al., A review of ultrasound detection methods for breast microcalcification, Math. Biosci. Eng., 16 (2019), 1761–1785. https://doi.org/10.3934/mbe.2019085 doi: 10.3934/mbe.2019085
    [10] M. N. S. K. B. Soulami, N. Kaabouch, A. Tamtaoui, Breast cancer: One-stage automated detection, segmentation, and classification of digital mammograms using U-net model based semantic segmentation, Biomed. Signal Process. Control, 2021 (2021), 102481. https://doi.org/10.1016/j.bspc.2021.102481 doi: 10.1016/j.bspc.2021.102481
    [11] Y. Wang, N. Wang, M. Xu, J. Yu, C. Qin, X. Luo, et al., Deeply-supervised networks with threshold loss for cancer detection in automated breast ultrasound, IEEE Trans. Med. Imaging, 39 (2019), 866–876. https://doi.org/10.1109/TMI.2019.2936500 doi: 10.1109/TMI.2019.2936500
    [12] E. H. Houssein, M. M. Emam, A. A. Ali, P. N. Suganthan, Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review, Exp. Syst. Appl., 167 (2021), 114161. https://doi.org/10.1016/j.eswa.2020.114161 doi: 10.1016/j.eswa.2020.114161
    [13] M. Xian, Y. Zhang, H. D. Cheng, F. Xu, B. Zhang, J. Ding, Automatic breast ultrasound image segmentation: A survey, Pattern Recognit., 79 (2018), 340–355. https://doi.org/10.1016/j.patcog.2018.02.012 doi: 10.1016/j.patcog.2018.02.012
    [14] Y. Tong, Y. Liu, M. Zhao, L. Meng, J. Zhang, Improved U-net MALF model for lesion segmentation in breast ultrasound images, Biomed. Signal Process. Control, 68 (2021), 102721. https://doi.org/10.1016/j.bspc.2021.102721 doi: 10.1016/j.bspc.2021.102721
    [15] D. Mishra, S. Chaudhury, M. Sarkar, A. S. Soin, Ultrasound image segmentation: A deeply supervised network with attention to boundaries, IEEE Trans. Biomed. Eng., 66 (2018), 1637–1648. https://doi.org/10.1109/TBME.2018.2877577 doi: 10.1109/TBME.2018.2877577
    [16] G. Chen, Y. Dai, J. Zhang, C-Net: Cascaded convolutional neural network with global guidance and refinement residuals for breast ultrasound images segmentation, Comput. Methods Programs Biomed., 2022 (2022), 107086. https://doi.org/10.1016/j.cmpb.2022.107086 doi: 10.1016/j.cmpb.2022.107086
    [17] N. K. Tomar, D. Jha, M. A. Riegler, H. D. Johansen, D. Johansen, J. Rittscher, et al., Fanet: A feedback attention network for improved biomedical image segmentation, Trans. Neural Networks Learn. Syst., 2022 (2022). https://doi.org/10.1109/TNNLS.2022.3159394 doi: 10.1109/TNNLS.2022.3159394
    [18] L. C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, A. L. Yuille, DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs, Trans. Pattern Anal. Mach. Intell., 2018 (2018), 834–848. https://doi.org/10.1109/TPAMI.2017.2699184 doi: 10.1109/TPAMI.2017.2699184
    [19] Y. Xie, J. Zhang, C. Shen, Y. Xia, Cotr: Efficiently bridging CNN and Transformer for 3d medical image segmentation, in Medical Image Computing and Computer Assisted Intervention MICCAI, (2021), 171–180. https://doi.org/10.1007/978-3-030-87199-4_16
    [20] N. S. Punn, S. Agarwal, RCA-IUnet: A residual cross-spatial attention-guided inception U-Net model for tumor segmentation in breast ultrasound imaging, Mach. Vision Appl., 33 (2022), 1–10. ttps://doi.org/10.1007/s00138-022-01280-3 doi: 10.1007/s00138-021-01257-8
    [21] N. Abraham, N. M. B. T. Khan, A novel focal tversky loss function with improved attention U-Net for lesion segmentation, Int. Symp. Biomed. Imaging, 2019 (2019), 683–687. https://doi.org/10.1109/ISBI.2019.8759329 doi: 10.1109/ISBI.2019.8759329
    [22] W. Jin, T. Derr, Y. Wang, Y. Ma, Z. Liu, J. Tang, Node similarity preserving graph convolutional networks, in Proceedings of the 14th ACM International Conference on Web Search and Data Mining, (2021), 148–156. https://doi.org/10.1145/3437963.3441735
    [23] B. Wu, X. Liang, X. Zheng, Y. Guo, H. Tang, Improving dynamic graph convolutional network with fine-grained attention mechanism, in ICASSP International Conference on Acoustics, Speech and Signal Processing (ICASSP), (2022), 3938–3942. https://doi.org/10.1109/ICASSP43922.2022.9746009
    [24] Y. Lu, Y. Chen, D. Zhao, J. Chen, Graph-FCN for image semantic segmentation, in Advances in Neural Networks–ISNN 2019: 16th International Symposium on Neural Networks, (2019), 97–105. https://doi.org/10.1007/978-3-030-22796-8_11
    [25] Y. Huang, Y. Sugano, Y. Sato, Improving action segmentation via graph-based temporal reasoning, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2020), 14024–14034. https://doi.org/10.1109/CVPR42600.2020.01404
    [26] W. Al-Dhabyani, M. Gomaa, H. Khaled, A. Fahmy, Dataset of breast ultrasound images, Data Brief, 28 (2020), 104863. https://doi.org/10.1016/j.dib.2019.104863 doi: 10.1016/j.dib.2019.104863
    [27] Z. Fu, J. Zhang, R. Luo, Y. Sun, D. Deng, L. Xia, TF-Unet: An automatic cardiac MRI image segmentation method, Math. Biosci. Eng., 19 (2022), 5207–5222. https://doi.org/10.3934/mbe.2022244 doi: 10.3934/mbe.2022244
    [28] X. Xu, M. Zhao, P. Shi, R. Ren, X. He, X. Wei, et al., Crack detection and comparison study based on faster R-CNN and mask R-CNN, Sensors, 22 (2022), 1215. https://doi.org/10.3390/s22031215 doi: 10.3390/s22031215
    [29] L. C. Chen, Y. Zhu, G. Papandreou, F. Schroff, H. Adam, Encoder-decoder with atrous separable convolution for semantic image segmentation, in Proceedings of the European Conference on Computer Vision (ECCV), (2018), 801–818.
    [30] R. Huang, M. Lin, H. Dou, Z. Lin, Q. Ying, X. Jia, et al., Boundary-rendering network for breast lesion segmentation in ultrasound images, Med. Image Anal., 80 (2022), 102478. https://doi.org/10.1016/j.media.2022.102478 doi: 10.1016/j.media.2022.102478
    [31] Z. Zhou, M. M. R. Siddiquee, N. Tajbakhsh, J. Liang, UNet++: A nested U-net architecture for medical image segmentation, in Lecture Notes in Computer Science, (2018), 3–11. http: /dx.doi.org/10.1007/ 978-3-030-00889-51
    [32] E. Sanderson, B. J. Matuszewski, FCN-Transformer feature fusion for polyp segmentation, in Medical Image Understanding and Analysis: 26th Annual Conference, Springer International Publishing, (2022), 892–907. https://doi.org/10.1007/978-3-031-12053-4_65
    [33] X. Feng, T. Wang, X. Yang, M. Zhang, W. Guo, W. Wang, ConvWin-UNet: UNet-like hierarchical vision transformer combined with convolution for medical image segmentation, Math. Biosci. Eng., 20 (2023), 128–144. https://doi.org/10.3934/mbe.2023007 doi: 10.3934/mbe.2023007
    [34] O. Oktay, J. Schlemper, L. L. Folgoc, M. Lee, M. Heinrich, K. Misawa, et al., Attention U-Net: learning where to look for the pancreas, preprint, arXiv: 1804.03999. https://doi.org/10.48550/arXiv.1804.03999
    [35] X. Zhang, K. Liu, K. Zhang, X. Li, Z. Sun, B. Wei, SAMS-Net: Fusion of attention mechanism and multi-scale features network for tumor infiltrating lymphocytes segmentation, Math. Biosci. Eng., 20 (2023), 2964–2979. https://doi.org/10.3934/mbe.2023140 doi: 10.3934/mbe.2023140
    [36] R. K. Meleppat, P. Zhang, M. J. Ju, S. K. K. Manna, Y. Jian, E. N. Pugh, et al., Directional optical coherence tomography reveals melanin concentration-dependent scattering properties of retinal pigment epithelium, J. Biomed. Optics, 24 (2019), 066011. https://doi.org/10.1117/1.JBO.24.6.066011 doi: 10.1117/1.JBO.24.6.066011
    [37] R. K. Meleppat, C. R. Fortenbach, Y. Jian, E. S. Martinez, K. Wagner, B. S. Modjtahedi, et al., In Vivo imaging of retinal and choroidal morphology and vascular plexuses of vertebrates using swept-source optical coherence tomography, Trans. Vis. Sci. Tech., 11 (2022), 11. https://doi.org/10.1167/tvst.11.8.11 doi: 10.1167/tvst.11.8.11
    [38] R. K. Meleppat, K. E. Ronning, S. J. Karlen, K. K. Kothandath, M. E. Burns, E. N. Pugh, et al., In situ morphologic and spectral characterization of retinal pigment epithelium organelles in mice using multicolor confocal fluorescence imaging, Invest. Ophthalmol. Vis. Sci., 61 (2020), 1. https://doi.org/10.1167/iovs.61.13.1 doi: 10.1167/iovs.61.13.1
    [39] J. He, Q. Zhu, K. Zhang, P. Yu, J. Tang, An evolvable adversarial network with gradient penalty for COVID-19 infection segmentation, Appl. Soft Comput., 113 (2021), 107947. https://doi.org/10.1016/j.asoc.2021.107947 doi: 10.1016/j.asoc.2021.107947
    [40] X. Liu, D. Zhang, J. Yao, J. Tang, Transformer and convolutional based dual branch network for retinal vessel segmentation in OCTA images, Biomed. Signal Process. Control, 83 (2023), 104604. https://doi.org/10.1016/j.bspc.2023.104604 doi: 10.1016/j.bspc.2023.104604
    [41] C. Zhao, A. Vij, S. Malhotra, J. Tang, H. Tang, D. Pienta, et al., Automatic extraction and stenosis evaluation of coronary arteries in invasive coronary angiograms, Comput. Biol. Med., 136 (2021), 104667. https://doi.org/10.1016/j.compbiomed.2021.104667 doi: 10.1016/j.compbiomed.2021.104667
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(2168) PDF downloads(103) Cited by(2)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog